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One challenge in today’s district heating systems is the relatively high distribution heat loss. Lowering distribution temperatures is one way to reduce operational costs resulting from high heat losses, while changing the distribution system from steel pipes to plastic pipes and changing the heat distribution concept can reduce investment costs. The result is that the overall life cycle cost of the district heating system is reduced, leading to the improved cost competitiveness of district heating versus individual heating options. The main aim of this study was to determine the most cost-efficient distribution system for a theoretical solar district heating system, by comparing the marginal life cycle cost of two different distribution systems. A secondary aim was to determine the influence of the employed pipe type and insulation level on the marginal life cycle cost by comparing detailed economic calculations, including differences in pipe installation costs and construction costs, among others. A small solar-assisted district heating system has been modeled in TRNSYS based on a real system, and this “hybrid” model is used as a basis for a second model where a novel distribution system is employed and the heating network operating temperature is changed. Results indicate that a novel distribution concept with lower network temperatures and central domestic hot water preparation is most efficient both from an energy and cost perspective. The total life cycle costs vary less than 2% for a given distribution concept when using different pipe types and insulation classes, indicating that the investment costs are more significant than operational costs in reducing life cycle costs. The largest difference in life cycle cost is observed by changing the distribution concept, the novel concept having approximately 24% lower marginal life cycle cost than the “hybrid” system. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Most of the district heating systems today use higher operating temperatures than those in new-built systems, possibly limiting compatibility with solar energy. This study evaluates the cost-effectiveness in terms of unit heat cost of integrating solar heating into an existing district heating system compared to not using solar energy, under changing economic boundary conditions such as collector and fuel cost, in addition to discount rate. This is investigated for both a scenario where the solar heating and a boiler replacement is done concurrently, as well as a scenario where solar heating is added to an existing system without replacing the boiler. A theoretical district heating supply of 3 MW is modelled and simulated based on a real system and load profile. The heat supply is varied to include storage with or without solar heating. Results for a 3 % discount rate indicate that; Replacing a 3 MW boiler with a slightly smaller boiler of 2.5 MW and adding a storage is cost effective and yields a unit heat cost of 58.0 EUR/MWh (16.1 EUR/TJ) which is a reduction of about 6 %. Installing solar heating together with the boiler replacement yields a unit heat cost as low as 55.7 EUR/MWh (15.4 EUR/GJ) which is a reduction of about 8 %. When replacing the boiler, all system configurations have similar unit heat costs compared to a boiler-only system, so factors such as emission reductions due to solar heating are relevant when considering alternatives. Furthermore, adding solar flat plate collectors corresponding to a 13 % solar fraction without replacing the boiler can reduce the unit heat cost as low as 34.8 EUR/MWh (9.7 EUR/TJ), which is 32 % lower than without solar. Evacuated tube collectors can increase this solar fraction to 17 % with similar system size, although at a higher cost. At a discount rate of 5 % solar heating is cost-competitive when fuel cost is above 26 EUR/MWh (7.2 EUR/TJ) and at 7 % competitive when fuel cost is above 32 EUR/MWh (8.9 EUR/TJ). Increasing solar heating system size reduces the backup-boiler fuel use during summer maintenance and makes fuel type less relevant for the overall unit heat cost. © 2024 The Authors

The high operating temperatures in today’s district heating networks combined with the low energy demand of new buildings lead to high relative network heat losses. New networks featuring lower operating temperatures have reduced relative heat losses while enabling an increase in the use of solar heat. The primary aim of this study was to determine if a particular district heating system can be made more effective with respect to heat losses and useful solar energy, by considering different distribution concepts and load densities. A small solar assisted district heating system with a novel hybrid distribution system has been modelled based on a real case study. This model serves as a basis for two other models where the distribution system and heating network operating temperature is changed. A secondary aim of the study was to determine the economic implications of making these changes, by using costs estimates to calculate the contribution of essential system components to total system cost. Results indicate that a novel distribution concept with lower network temperatures and central domestic hot water preparation is most energy efficient in a sparse network with a heat density of 0.2 MWh/m∙a and a performance ratio of 66%, while a conventional district heating system performs worst and has a performance ratio of less than 58% at the same heat density. In an extremely sparse network with heat density of 0.05 MWh/m∙a, the performance ratio is 41% and 30% for these systems, respectively. A simple economic analysis indicates that the novel distribution concept is also best from an economic point of view, reducing the initial investment cost by 1/3 compared to the conventional concept, which is the most costly. However, more detailed calculations are needed to conclude on this.

Bipolar supercapacitors that can store many fold higher capacitance in negative voltage compared to positive voltage are of great importance if they can be engineered for practical applications. The electrode material encompassing high surface area, better electrochemical stability, high conductivity, moderate distribution of pore size, and their interaction with suitable electrolytes is imperative to enable bipolar supercapacitor performance. Apropos of the aforementioned aspects, the intent of this work is to ascertain the effect of ionic properties of different electrolytes on the electrochemical properties and performance of a porous CNT-MoS2 hybrid microstructure toward bipolar supercapacitor applications. The electrochemical assessment reveals that the CNT-MoS2 hybrid electrode exhibited a two- to threefold higher areal capacitance value of 122.3 mF cm-2 at 100 μA cm-2 in 1 M aqueous Na2SO4 and 42.13 mF cm-2 at 0.30 mA cm-2 in PVA-Na2SO4 gel electrolyte in the negative potential window in comparison to the positive potential window. The CNT-MoS2 hybrid demonstrates a splendid Coulombic efficiency of ∼102.5% and outstanding stability with capacitance retention showing a change from 100% to ∼180% over 7000 repeated charging-discharging cycles. © 2023 American Chemical Society.

Water, which is predicted to be one of the most critical resources for the near future, also plays a vital role in society's sustainable development. Wastewater treatment is a critical part of the circular water management system and offers various technological alternatives. Taking appropriate decision for the technology selection is, therefore, essential for a long-term perspective. A complex yet imperative process is the sustainable selection of the wastewater treatment process. This paper presents the use of multi-criteria decision-making (MCDM) in the sustainability assessment of wastewater treatment technologies that may be very relevant to the growing sector with many emerging options. A comparison of six wastewater treatment technologies based on four sustainability parameters using three MCDM techniques, namely FSWARA, FMOORA and FTOPSIS is presented in detail. FSWARA is used for weighting criteria and the other two for technology ranking. The detailed step-by-step comparison study is presented and the results were somewhat predictable for the study, and this confirms the reliability of the methodology. This paper's primary objective is to propose a well-defined increscent practice for making sustainable wastewater treatment decisions among state-of-the-art technologies.

Dispersion of nanoparticles is one of the potential solutions to improve the thermophysical properties of phase change (or transition) materials (PCMs) and enhance the performance of latent thermal energy storage (LTES) systems. The PCM ought to have a high latent heat of fusion, and zero or negligible coefficient of thermal expansion. A good PCM should have melting and solidification compatibility with negligible or zero subcooling, and it should not react with the common chemical reagents. The present known PCMs possess low thermal conductivity that results into a longer solidification and melting time of PCMs. In the past two decades, researchers have reported improved thermal conductivity and heat-storing capacity of PCMs employing graphite nanoparticles/fibers, carbon nanotubes/fibers, metal, and metal oxide nanoparticles. This work reviews the reported experimental and numerical studies describing the consequences of nanoparticle inclusions of various shapes and sizes on the thermal properties of the PCMs. This review attempts to make a consolidated database of the studies related to nanoadditive inclusion into PCMs for various applications. Graphene dispersed into PCM has resulted into 14 times thermal conductivity enhancement. As far as metal oxide nanoparticles are concerned, TiO2 and Al2O3 nanoparticles outperformed others. The compatibility between the nanoadditive and PCM is necessary to tailor favorable thermal properties. This work reviews numerous studies of different nanoparticle-PCM duos. © 2021 American Chemical Society.

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios. © 2024 The Author(s)

The applications of 3D printing are rapidly increasing in aerospace and naval applications. Nonetheless, 3D printing (3DP) of graded foams exhibiting property variation along the thickness direction is yet to be explored. In the current work, the different volume fractions of hollow glass micro balloon (GMB) reinforced high-density polyethylene (HDPE) composite based graded foams are 3D printed using the fused deposition modelling (FDM) technique. The bonding between successive layers and porosity distribution of these graded configurations are studied using micro-CT scan. Further, the 3D Printed functionally graded foams (FGFs) are tested for flexural response, and results are compared with numerical values. The micro-CT results showed delamination absence between the layers. In neat HDPE layers, porosity is not evident, while minor porosity creeps in the layers having the highest GMB content. Experimental results of the flexural test showed that the graded sandwiches exhibited better strength than the graded core alone. Compared to neat HDPE, the modulus of FGF-2 (H20–H40–H60) increased by 33.83%, implying better mechanical stiffness. Among all the FGFs, FGF-2 exhibited a better specific modulus. A comparative study of experimental and numerical results showed a slight deviation due to neglecting the induced porosity. © 2023 The Authors

Nowadays, PM2.5 concentrations greatly influence indoor air quality in subways and threaten passenger and staff health because PM2.5 not only contains heavy metal elements, but can also carry toxic and harmful substances due to its small size and large specific surface area. Exploring the physicochemical and distribution characteristics of PM2.5 in subways is necessary to limit its concentration and remove it. At present, there are numerous studies on PM2.5 in subways around the world, yet, there is no comprehensive and well-organized review available on this topic. This paper reviews the nearly twenty years of research and over 130 published studies on PM2.5 in subway stations, including aspects such as concentration levels and their influencing factors, physicochemical properties, sources, impacts on health, and mitigation measures. Although many determinants of station PM2.5 concentration have been reported in current studies, e.g., the season, outdoor environment, and station depth, their relative influence is uncertain. The sources of subway PM2.5 include those from the exterior (e.g., road traffic and fuel oil) and the interior (e.g., steel wheels and rails and metallic brake pads), but the proportion of these sources is also unknown. Control strategies of PM mainly include adequate ventilation and filtration, but these measures are often inefficient in removing PM2.5. The impacts of PM2.5 from subways on human health are still poorly understood. Further research should focus on long-term data collection, influencing factors, the mechanism of health impacts, and PM2.5 standards or regulations.

The Heating, Ventilation and Air Conditioning (HVAC) systems are complex and prone to failures during operation, often leading to significant energy waste. Timely and accurate Fault Detection and Diagnosis (FDD) can enhance energy efficiency. The HVAC system operates under diverse conditions, data-driven models trained under existing conditions may experience performance degradation when faced with new conditions. Transfer learning offers an effective solution to this issue. This study proposes a novel transfer learning ensemble model based on adaptive weights, leveraging different transfer learning strategies to improve diagnosis performance under new conditions. Multiple cross-condition transfer learning tasks were implemented to test the proposed method, and its effectiveness was validated through multiple experiments to minimize the impact of randomness. Results showed that, compared to fine-tuning (FT), domain-adversarial neural network (DANN), and baseline models, the proposed method outperforms the other models. The average accuracy of multiple experiments improved by 0.21 % to 2.34 % compared to FT. Additionally, modifying DANN to utilize a small amount of labeled information from the target domain has led to greater overlap between the feature distributions of the source and target domains, resulting in improved performance that is close to that of FT. Finally, we analyzed the impact of target domain data volume on the performance of the four methods. The performance of the baseline model improved significantly with the increase in data volume, while the other models showed less improvement. Meanwhile, the diagnostic results of the baseline model were significantly influenced by experimental randomness when there is less training data, whereas the FT diagnostic results were relatively more stable. © 2024 Elsevier B.V.

Due to the broad adoption of electronic and electrical equipment and the quick advancement of contemporary innovations in this domain, significant amounts of electronic waste have been produced. This category of waste includes the toner powder used by printers, copiers, and fax machines to print text and images. This paper describes a sustainable and environmentally friendly method of recycling waste toner powder. The chemical composition of this printer cartridge toner (PCt) powder is carbon, Fe3O4, polypropylene (polymeric resin), and SiO2 composite. Toner powder from exhausted printer cartridges was utilized as an adsorbent to remove fluoride from water. It has a fluoride adsorption capacity of 60 mg/g and a specific surface area of 20 m2/g. X-ray diffraction and electron microscopic investigations were used to investigate the chemical composition, structure, and surface morphology of the material. To analyze the collected experimental data, the Freundlich and Langmuir adsorption isotherm models were used. Time-dependent kinetic experiments were conducted to determine the mechanism of the adsorption process using pseudo-first-order kinetics, pseudo-second-order kinetics, and intraparticle diffusion kinetic models. The fluoride adsorption process was shown to be feasible and spontaneous (ΔG < 0) based on calculated thermodynamic characteristics, which included enthalpy, Gibbs free energy, entropy (ΔS > 0), and adsorption activation energy. The study also discussed its reusability as an adsorbent and examined its functioning capability in actual water. © 2024 The Authors

The current work focuses on predicting the buckling and free vibration frequencies (fn) of cenosphere reinforced epoxy based syntactic foam beam under varying loads. Critical buckling loads (Ncr) and fn are predicted using the differential quadrature method (DQM). Ncr and fn have been calculated for beams of varying cenosphere volume fractions subjected to axial load under clamped-clamped (CC), clamped-simply (CS), simply-simply (SS), and clamped-free (CF) boundary conditions (BC′s). Upon increasing the cenosphere volume fraction, Ncr and fn of syntactic foam composites increases. These numerical outcomes are compared with the theoretical values evaluated through the Euler-Bernoulli hypothesis and further compared with experimental outcomes. Results are observed to be in precise agreement. The results of the DQM numerical analysis are given out for the different BC′s, aspect ratios, cenosphere volume fractions, and varying loads. It is perceived that depending on the BC′s, the type of axial varying loads and aspect ratios has a substantial effect on the Ncr and fn behaviour of the syntactic foam beams. A comparative study of the obtained results showed that the beam subjected to parabolic load under CC boundary conditions exhibited a higher buckling load. © 2023 The Authors

Grid-connected PV battery systems for private homes are becoming increasingly popular in many countries, including Sweden. This study aimed to evaluate the techno-economic feasibility of such distributed, grid-connected PV battery systems for single homes at a Swedish holiday location. It was especially of interest to investigate the impact of demand charges, as they are frequently introduced by utilities in Sweden and are also common in popular winter sport regions. Grid-connected PV battery systems were sized and optimized based on their net present cost. Load patterns, incentives, demand tariff structures and electricity price variation were used to study the sensitivity of the obtained results. Grid-connected residential PV battery systems were found to be equally profitable compared to grid-connected PV systems without batteries when demand charges were applied. When the load profiles had peak loads throughout the whole year and the batteries were large enough sized to shave many peaks, grid-connected PV battery systems had slightly higher profitability than grid-connected PV systems without batteries. The total savings also depended on the actual rate of demand charge. The good profitability we found greatly depends on the current state incentives for these systems in the form of tax credits for surplus electricity and investment costs. Removing the tax credit for surplus electricity would reduce the savings generated by a grid-connected PV system without batteries significantly more than for grid-connected PV systems with batteries.

In this work we present a method to evaluate the compatibility of a solar thermal integration in an industrial process. We introduce two indicators to quantify in a comprehensive way all the financial and technicalboundaries influence on the design choices and on the costs of a solar thermal integration.Both the indicators are measures of the divergence of an integration from a fictive (yet technically possible)ideal case and from a “do-nothing” case. In this way, the incompatibilities of the integration are associated tothe consequences that the non-ideal factors have on both costs and performances. Moreover, the value of the indicators is normalized between a minimum and a maximum being the “do-nothing” and the ideal cases respectively.We then used these indicators on a real case scenario to compare the pros and cons of two different solarthermal integration approaches (hot water vs steam) to a beer brewery in southern Europe.The results show that retrofitting part of the existing appliance to be fed with hot water rather than steamenhances the compatibility of solar thermal with the brewery. Nevertheless, we measured no relevant improvement to the compatibility of solar thermal when designing a brewery from scratch with the same characteristics but were the solar thermal system could have been integrated “ad-hoc” rather than retrofitted

Net-zero energy buildings (NZEBs) are considered as a promising method to mitigating the energy problems. Due to the intermittent characteristics of renewable energy (e.g., solar energy), NZEBs need to frequently exchange energy with the grid, which imposes severe negative impacts on the grid especially the overvoltage risk. Both planning and design are essential for reducing NZEB connected grid overvoltage, but most existing studies isolated the efforts from planning to design, thereby failing to achieve the best cumulative result. More importantly, existing studies oversimplified overvoltage quantification by using aggregated power interactions to represent overvoltage risk, which cannot consider the complex voltage influences among grid nodes. Due to the isolated efforts and the quantification oversimplification, existing studies can hardly achieve overvoltage risk minimization. Therefore, this study proposes a novel GA (genetic algorithm)-based method in which the key planning and design parameters are optimized sequentially for mitigating the overvoltage risk. Meanwhile, distribution network model has been adopted to precisely quantify the grid overvoltage. The study results show that the proposed method is highly effective in reducing NZEB cluster connected grid overvoltage risk. The proposed method can be used in practice for improving NZEB cluster planning and system design as grid interaction is considered. © 2021 Elsevier Ltd

A convertible, zoned ventilation system was field-tested in a modern, airtight Swedish home when occupied either by an experimental team or by a family. Indoor air quality in the master bedroom was monitored under four ventilation strategies. Relative to constant air volume strategies (CAV), demand-controlled ventilation (DCV) that was responding to CO2 concentration extracted more air when people were present, but less in total over 24 h. This elevated the indoor air humidity, beneficial in climates with dry winter air. Multiple monitors within the bedroom indicated that vertical CO2 stratification occurred routinely, presumably due to low mixing of supply air from a wall-mounted diffuse vent, spreading the air radially over the wall. This seemingly improved air quality in the breathing zone under local (ceiling) extract ventilation but worsened it during more typical, centralised extract ventilation, where air escapes the room via an inner doorway. The local extract arrangement thus seemed to yield both improved ventilation efficiency and reduced contaminant spread to other rooms. The noted air quality variations within the room highlight the importance of sensor placement in demand-control ventilated spaces, even in small rooms such as bedrooms.

A field study conducted in a modern Nordic single-family house with high airtightness and insulation levels, attempted to control summer indoor overheating using night-time cooling strategies. Exhaust air flow rates were manually scheduled by the researchers (based on weather forecasts), analogous to what an engaged occupant - or a predictive system - might do. Air temperatures at a nearby meteorological station peaked at 30 °C during 6 days in June that saw only 44 hours below 18 °C. Temperatures recorded indoors at the test house reached 32 °C, due also to very large solar gains, and never fell below 26 °C over 8 continuous days. It appears that under extended heat conditions that are exceptional now, but foreseen to become more frequent, some modern Nordic homes cannot be temperature controlled by ambient ventilation alone. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Industrial heat production is responsible for around 20 % of total greenhouse gas emissions in Europe. To achieve the climate change goals defined in Paris Climate Agreement, reducing greenhouse gas emissions from industry are urgently needed. Therefore, providing sustainable heat production for industrial applications would be considered as an essential solution. The ongoing conflict between Russia and Ukraine has further exposed EU for their dependency of natural gas in Russia. The EU commission has shifted their focus on sustainable means to generate heating in both residential and industrial applications. For fulfilling this target, focus and develop in novel technologies using renewable sources which could help to minimize CO2 emissions in their life cycle is inevitable.

Among few existing technologies, this report, focuses on two technologies which hold strong potential for heating decarbonization. High-temperature heat pumps (HTHP) which is a technology currently under development and already reached to cover up to 160 °C applications. It is projected that heat pumps would play bigger role with their higher rate of performance in future for industrial heat problem as they have potential to be totally carbon free technology, when they are hybridized with electricity from renewable sources. Solar thermal (ST) technology and specifically parabolic trough collector (PTC) which has a longer history of implementation in industrial energy systems and have obtained significant attention in ST heating systems for industrial applications. Solar thermal system can produce heat economically and at minimal carbon footprint compare to other technologies in the market.

The main aim of this thesis is based on implementing of mentioned technologies and evaluate their levelized cost of heat (LCOH) individually and consequently their capabilities in providing industrial heat loads. To be more precise, this thesis aims are to fulfil a comparative techno-economic analysis using PTC and HTHP. Boundary conditions for geographical constrains in Europe have been applied for setting up cases for further analysis. Then a sensitivity analysis by manipulating different parameters is made.  It is important to mention that simulations for HTHP are performed using Excel spread sheet, while for ST part TRNSED and OCTAVE are used. 

Results shows the cost of heat generation for both HTHP and PTC collectors with changing boundary conditions. A  maximum solar fraction (SF) limit of PTC collector is defined to indicate when the LCOH for these two technologies coincides. The results can support decision-makers/designers to have a rough estimation of which SF would be viable under different boundaries such as: Direct Normal Irradiation (DNI) level, Capital expenses (CAPEX) of HTHP, load profile, and electricity price.

The results from the work can be used as a stepping stone to bring collaboration between two technologies thus working as hybrid system to provide nearly renewable heating systems for industries.

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments. © 2023 Institute of Physics Publishing. All rights reserved.

In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

Household electricity demand has substantial impacts on local grid operation, energy storage, and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process, and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this chapter proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

To effectively reduce building energy consumption, a novel full fresh air system with a heat source tower (HST) and a borehole heat exchanger (BHE) was proposed for space cooling and dehumidification in this paper. The cooling system only adopts geothermal energy to produce dry and cold fresh air for space cooling and dehumidification through the BHE and HST, which has the advantage of non-condensate water compared to BHE systems integrated with a fan coil or chilled beam. Based on the established mathematical model of the cooling system, this paper analyzed the system characteristics, feasibility, operation strategy, energy performance, and cost-effectiveness of the proposed model in detail. The results show that the mathematical model has less than 10% error in estimating the system performance compared to the practical HST-BHE experimental set up. Under the specific boundary conditions, the cooling and dehumidification capacity of this system increases with the decrease in the air temperature, air moisture content, and inlet water temperature of the HST. The optimal cooling capacity and the system COP can be achieved when the air-water flow ratio is at 4:3. A case study was conducted in a residential building in Shenyang with an area of about 1800 m2. It was found that this system can fully meet the cooling and dehumidification demand in such a residential building. The operation strategy of the cooling system can be optimized by adjusting the air-water flow ratio from 4:3 to 3:2 during the early cooling season (7 June-1 July) and end cooling season (3 August-1 September). As a result, the average COP of the cooling system during the whole cooling season can be improved from 6.1 to 8.7. Compared with the air source heat pump (ASHP) and the ground source heat pump (GSHP) for space cooling, the proposed cooling system can achieve an energy saving rate of 123% and 26%, respectively. Considering that the BHE of the GSHP can be part of the proposed HST-BHE cooling system, the integration of the HST and GHSP for space cooling (and heating) is strongly recommended in actual applications.

At a global level, the need for energy efficiency and an increased share of renewable energy sources is evident, as is the crucial role of cities due to the rapid urbanization rate. As a consequence of this, the research work related to Positive Energy Districts (PED) has accelerated in recent years. A common shared definition, as well as technological approaches or methodological issues related to PEDs are still unclear in this development and a global scientific discussion is needed. The International Energy Agency’s Energy in Buildings and Communities Programme (IEA EBC) Annex 83 is the main platform for this international scientific debate and research. This paper describes the challenges of PEDs and the issues that are open for discussions and how the Annex 83 is planned and organized to facilitate this and to actively steer the development of PEDs major leaps forward. The main topics of discussion in the PED context are the role and importance of definitions of PEDs, virtual and geographical boundaries in PEDs, the role of different stakeholders, evaluation approaches, and the learnings of realized PED projects.

High efficacy LED lamps combined with adaptive lighting control and greenhouse integrated photovoltaics (PV) could enable the concept of year-round cultivation. This concept can be especially useful for increasing the production in the Nordic countries of crops like herbaceous perennials, forest seedlings, and other potted plants not native of the region, which are grown more than one season in this harsh climate. Meteorological satellite data of this region was analyzed in a parametric study to evaluate the potential of these technologies. The generated maps showed monthly average temperatures fluctuating from −20 °C to 20 °C throughout the year. The natural photoperiod and light intensity also changed drastically, resulting in monthly average daily light integral (DLI) levels ranging from 45–50 mol·m⁻²·d⁻¹ in summer and contrasting with 0–5 mol·m⁻²·d⁻¹ during winter. To compensate, growth room cultivation that is independent of outdoor conditions could be used in winter. Depending on the efficacy of the lamps, the electricity required for sole-source lighting at an intensity of 300 µmol·m⁻²·s⁻¹ for 16 h would be between 1.4 and 2.4 kWh·m⁻²·d⁻¹. Greenhouses with supplementary lighting could help start the cultivation earlier in spring and extend it further into autumn. The energy required for lighting highly depends on several factors such as the natural light transmittance, the light threshold settings, and the lighting control protocol, resulting in electric demands between 0.6 and 2.4 kWh·m⁻²·d⁻¹. Integrating PV on the roof or wall structures of the greenhouse could offset some of this electricity, with specific energy yields ranging from 400 to 1120 kWh·kW⁻¹·yr⁻¹ depending on the region and system design.

Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future. © 2022 The Author(s)

Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar Photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this paper conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This study can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications. © 2021 The Author(s)

Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this chapter conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This chapter can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 realworld charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios. © 2022 Elsevier Ltd

Collaborations among nZEBs (e.g., renewable energy sharing) can improve nZEBs’ performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this chapter proposes a clustering-based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results show that the developed method is effective in improving nZEBs’ performance at the community level. The proposed method will provide the decision makers a means to group nZEBs, which maximizes the collaboration benefits and thus assists the planning of nZEB communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel geographic information system (GIS)-assisted optimal design method for renewable-powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable-powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user-defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Nearly zero energy buildings (NZEBs) are considered as a promising solution to the mitigation of the energy problems. A proper control of the energy system operation of the nZEB cluster is essential for improving load matching, reducing grid interaction and reducing energy bills. Existing studies have developed many demand response control methods to adjust the operation of energy systems to improve performances. Most of these studies focus on optimizing performances at individual-nZEB-level while neglecting collaborations (e.g., energy sharing and battery sharing) between nZEBs. Only a few studies consider the collaborations and optimize the system operation at nZEB-cluster-level, yet they cannot take full advantage of nZEB collaborations as optimization is conducted in a bottom-up manner lacking global coordination. This chapter, therefore, proposes a top-down control method of nZEBs for optimizing performances at the cluster level. The top-down control method first considers the nZEB cluster as ‘one’ and optimizes its energy system operation using the genetic algorithm (GA), and then it coordinates the operation of every single nZEB inside the cluster using non-linear programming (NLP). The top-down control enables collaborations among nZEBs by coordinating single nZEB’s operations. Such collaborations can bring significant performance improvements in different aspects. For instance, in aspect of economic cost, the collaborations can reduce the high-priced energy imports from the grid by sharing the surplus renewable energy with nZEBs which have insufficient energy generations. The proposed top-down control has been compared with a traditional non-collaborative control. The study results show that the top-down control is effective in improving performances at cluster level. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to keep increasing in the future. The large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures, as they are not designed to host such large power flow. The use of smart EV charging can be a resource-efficient and cost-effective way to enhance the local power balance, and it can mitigate power issues while avoiding expensive upgrading of the existing infrastructure. In this regard, some studies have developed dedicated EV smart charging controls. Most of the existing EV smart charging controls can be categorized into three approaches according to their optimization principles: individual, bottom-up, and top-down. Until now, systematic comparison and analysis of the different approaches are still lacking. It is still unknown whether a control approach performs better than others and, if yes, why is it so. This chapter aims to fill in such knowledge gaps by conducting a systematic comparison of these three different control approaches and analyzing their performances in depth. A representative control algorithm will be selected from each control approach; then, the selected algorithms will be applied for optimizing EV charging loads in a building community in Sweden. Their power regulation performances will be comparatively investigated in two perspectives: minimize peak power exchanges with the grid and maximize PV power self-consumption. The computational performances are also investigated. The study results show that the top-down coordinated approach is superior to bottom-up coordinated approach and individual independent approach in terms of demand response performances, nevertheless the computational loads are much higher. This may make the convergence difficult. Control strategies also have large impacts on the power regulation performances. This chapter will help pave the way for the developments of more sophisticated control algorithms for EV smart charging in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Near-zero energy buildings (nZEBs) are considered as an effective solution to mitigating CO2 emissions and reducing the energy usage in the building sector. A proper sizing of the nZEB systems (e.g. HVAC systems, energy supply systems, energy storage systems, etc.) is essential for achieving the desired annual energy balance, thermal comfort, and grid independence. Two significant factors affecting the sizing of nZEB systems are the uncertainties confronted by the building usage condition and weather condition, and the degradation effects in nZEB system components. The former factor has been studied by many researchers; however, the impact of degradation is still neglected in most studies. Degradation is prevalent in energy components of nZEB and inevitably leads to the deterioration of nZEB life-cycle performance. As a result, neglecting the degradation effects may lead to a system design which can only achieve the desired performance at the beginning several years. This chapter, therefore, proposes a life-cycle performance analysis (LCPA) method for investigating the impact of degradation on the longitudinal performance of the nZEBs. The method not only integrates the uncertainties in predicting building thermal load and weather condition, but also considers the degradation in the nZEB systems. Based on the proposed LCPA method, a two-stage method is proposed to improve the sizing of the nZEB systems. The study can improve the designers’ understanding of the components’ degradation impacts and the proposed method is effective in the life-cycle performance analysis and improvements of nZEBs. It is the first time that the impacts of degradation and uncertainties on nZEB LCP are analysed. Case studies show that an nZEB might not fulfil its definition at all after some years due to component degradation, while the proposed two-stage design method can effectively alleviate this problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

The use of electric vehicles (EVs) has been on the rise. Most of the existing EV smart charging controls can be categorized into three approaches according to their optimization principles: individual, bottom-up and top-down. Until now, systematic comparison and analysis of the different approaches are still lacking. It is still unknown whether a control approach performs better than others and, if yes, why is it so. This study aims to fill in such knowledge gaps by conducting a systematic comparison of these three different control approaches and analyzing their performances in depth. A representative control algorithm will be selected from each control approach, then the selected algorithms will be applied for optimizing EV charging loads in a building community in Sweden. Their power regulation performances will be comparatively investigated. This study will help pave the way for the developments of more sophisticated control algorithms for EV smart charging. © 2022 The Authors, published by EDP Sciences.

Proper energy storage system design is important for performance improvements in solar power shared building communities. Existing studies have developed various design methods for sizing the distributed batteries and shared batteries. For sizing the distributed batteries, most of the design methods are based on single building energy mismatch, but they neglect the potentials of energy sharing in reducing battery capacity, thereby easily causing battery oversizing problem. For sizing the shared batteries, the existing design methods are based on a community aggregated energy mismatch, which may avoid battery oversizing but cause another severe problem, i.e. excessive electricity losses in the sharing process caused by the long-distance power transmissions. Therefore, this chapter proposes a hierarchical design method of distributed batteries in solar power shared building communities, with the purpose of reducing the battery capacity and minimizing the energy loss in the sharing process. The developed design method first considers all the distributed batteries as a virtual ‘shared’ battery and searches its optimal capacity using genetic algorithm. Taking the optimized capacity as a constraint, the developed method then optimizes the capacities of the distributed batteries for minimizing the energy loss using nonlinear programming. Case studies on a building community show that compared with an existing design method, the proposed design can significantly reduce the battery capacity and electricity loss in the sharing process, i.e. 36.6% capacity reduction and 55% electricity loss reduction. This chapter integrates the considerations of aggregated energy needs, local PV power sharing, advanced community control, and battery storage sharing, which will be useful to optimize three functions (energy efficiency, energy production and flexibility) in a positive energy district towards energy surplus and climate neutrality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Model-based real-time optimization (MRTO) is proven as an effective tool that can capture the complex dynamics of heating, ventilation, and air conditioning (HVAC) systems and improve its energy performance. Despite the energy benefits offered by MRTO, these approaches are rarely implemented in actual buildings. This is due to the reason that these approaches are very difficult to implement because they require the synthesis of a reliable and accurate performance model of the system. The reliability of decision-making with MRTO is directly related to the accuracy of these performance models. In addition, the model has to be computationally efficient for practical implementation. The development of such a model requires the most effort and is a major challenge in the implementation of MRTO. Several HVAC performance models are already available in the literature, and these can be classified as semiphysical models and data-driven models. The semiphysical models are generalized models with simplification assumptions that can provide consistent performance, however, with reduced accuracy. Contrastingly, the data-driven models can offer better accuracy; however, they lack robustness in terms of operational ranges. These factors affect the energy performance of MRTO, and an improper parametrized model could result in performance that is even worse than the conventional fixed setpoint or rule-based approaches. A dissimilarity-driven ensemble model-based real-time optimization (DEMRTO) approach is presented in this study that incorporates a dissimilarity-driven ensemble model in the framework of real-time optimization. The dissimilarity-driven ensemble model combines semiphysical models and data-driven models in a systematic manner to use one's strengths to address others' weaknesses, rather than developing a new form of a model. The performance of the proposed integrated approach was examined using case studies over three weather seasons in Hong Kong. The results showed as compared to the fixed setpoint approach the DEMRTO approach can provide significant energy savings up to 11.085% setpoint, and around 2.785% reduction in energy use as compared with the conventional MRTO approach. It was demonstrated that the proposed approach can capture diversity in load conditions and provide consistency in model prediction to improve reliability in decision-making with real-time optimization.

In recent years, perovskite solar cells (PSCs) have achieved a remarkable power conversion efficiency of 25.5%, indicating that they are a promising alternative to dominant Si photovoltaic (PV) technology. This technology is expected to solve the world's energy demand with minimal investment and very low CO2 emissions. The market has shown a lot of interest in PSCs technology. A technoeconomic analysis is a useful tool for tracking manufacturing costs and forecasting whether technology will eventually achieve market-driven prices. A technoeconomic analysis of a 100 MW carbon-based perovskite solar module (CPSM) factory located in India is presented in this paper. Two CPSMs architectures-high-temperature processed CPSMs (Module A) and low-temperature processed CPSM's (Module B)-are expected to offer minimum sustainable prices (MSPs) of $ 0.21 W-1 and $ 0.15 W-1. On the basis of MSP, the levelized cost of energy (LCOE) is calculated to be 3.40 (sic) kWh(-1) for module A and 3.02 (sic) kWh(-1) for module B, with a 10-year module lifetime assumption. The same modules with a 25-year lifespan have LCOEs of 1.66 and 1.47 (sic) kWh(-1), respectively. These estimates are comparable to market dominant crystalline silicon solar modules, and they are also favorable for utilizing perovskite solar cell technology.