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Hybrid Artificial Neural Networks Based Models for Electricity Spot Price Forecasting - A Review
Dalarna University, School of Technology and Business Studies, Energy Technology.
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2019 (English)In: 2019 16th European Energy Market Conference (EEM 19), 2019, article id 8916245Conference paper, Published paper (Refereed)
Abstract [en]

Electricity price forecasting plays a crucial role in aliberalized electricity market. In terms of forecasting approaches,artificial neural networks are the most popular amongresearchers due to their flexibility and efficiency in handlingcomplexity and non-linearity. On the other hand, a single neuralnetwork presents certain limitations. Therefore, in recent years,hybrid models that combine multiple algorithms to balance outthe advantages of a single model have become a trend. However,a review of recent applications of hybrid neural networks basedmodels with respect to electricity price forecasting is not found inthe literature and hence, the motivation of this paper is to fill thisresearch gap. In this study, methodologies of existing forecastingapproaches are briefly summarized, followed by reviews of neuralnetwork based hybrid models concerning electricity forecastingfrom year 2015 onwards. Major contributions of each study,datasets adopted in experiments as well as the correspondingexperiment results are analyzed. Apart from the review ofexisting studies, the novelty and advantages of each type of hybridmodel are discussed in detail. Scope of the review is theapplication of hybrid neural network models. It is found that theforecast horizon of the reviewed literature is either hour ahead orday ahead. Medium and long term forecasting are notcomprehensively studied. In addition, though hybrid modelsrequire relatively large computational time, time measurementsare not reported in any of the reviewed literature.

Place, publisher, year, edition, pages
2019. article id 8916245
Keywords [en]
price forecasting, neural networks, electricity markets, computational intelligence, machine learning
National Category
Economics and Business Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-30948DOI: 10.1109/EEM.2019.8916245Scopus ID: 2-s2.0-85076693516OAI: oai:DiVA.org:du-30948DiVA, id: diva2:1379712
Conference
19th European Energy Market Conference (EEM 19), University of Ljubljana, 18-20 September 2019
Available from: 2019-10-15 Created: 2019-12-17 Last updated: 2020-01-22Bibliographically approved

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Zhang, FanFleyeh, Hasan

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