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Household’s energy consumption and productionforecasting: A Multi-step ahead forecast strategiescomparison.
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
2017 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
Abstract [en]

In a changing global energy market where the decarbonization of the economy and

the demand growth are pushing to look for new models away from the existing

centralized non-renewable based grid. To do so, households have to take a

‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead

forecast of their expected energy production and consumption. In multi-step ahead

forecasting there are different strategies to perform the forecast. The single-output:

Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis

performs a comparison between the performance of the differents strategies in a

‘prosumer’ household; using Artificial Neural Networks, Random Forest and

K-Nearest Neighbours Regression to forecast both solar energy production and

grid input. The results of this thesis indicates that the methodology proposed

performs better than state of the art models in a more detailed household energy

consumption dataset. They also indicate that the strategy and model of choice is

problem dependent and a strategy selection step should be added to the forecasting

methodology. Additionally, the performance of the Recursive strategy is always

far from the best while the DIRMO strategy performs similarly. This makes the

latter a suitable option for exploratory analysis.

Ort, förlag, år, upplaga, sidor
2017.
Nyckelord [en]
Multi-step, forecast, strategies, Recursive, Direct, DirRec, DIRMO, MIMO, Artificial Neural Networks, Random Forest, K-Nearest Neighbours Regression, MAPE, MAE
Nationell ämneskategori
Tvärvetenskapliga studier inom samhällsvetenskap
Identifikatorer
URN: urn:nbn:se:du-25849OAI: oai:DiVA.org:du-25849DiVA, id: diva2:1135425
Tillgänglig från: 2017-08-23 Skapad: 2017-08-23 Senast uppdaterad: 2018-01-13

Open Access i DiVA

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