Evaluation of Neural Networks for Residential Load Forecasting and the Impact of Systematic Feature Identification

Nicolai Bo Vanting*, Zheng Grace Ma, Bo Nørregaard Jørgensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Energy systems face challenges due to climate change, distributed energy resources, and political agenda, especially distribution system operators (DSOs) responsible for ensuring grid stability. Accurate predictions of the electricity load can help DSOs better plan and maintain their grids. The study aims to test a systematic data identification and selection process to forecast the electricity load of Danish residential areas. The five-ecosystem CSTEP framework maps relevant independent variables on the cultural, societal, technological, economic, and political dimensions. Based on the literature, a recurrent neural network (RNN), long-short-term memory network (LSTM), gated recurrent unit (GRU), and feed-forward network (FFN) are evaluated and compared. The models are trained and tested using different data inputs and forecasting horizons to assess the impact of the systematic approach and the practical flexibility of the models. The findings show that the models achieve equal performances of around 0.96 adjusted R 2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R 2 of around 0.91 and increased the error slightly to 6–7% absolute percentage error. The impact of the systematic identification approach depended on the type of neural network, with the FFN showing the highest increase in error when removing the supporting variables. The GRU and LSTM did not rely on the identified variables, showing minimal changes in performance with or without them. The systematic approach to data identification can help researchers better understand the data inputs and their impact on the target variable. The results indicate that a focus on curating data inputs affects the performance more than choosing a specific type of neural network architecture.

Original languageEnglish
Article number63
JournalEnergy Informatics
Volume5
Issue numberSuppl. 4
Number of pages23
ISSN2520-8942
DOIs
Publication statusPublished - 21. Dec 2022
EventEnergy Informatics.Academy Conference 2022 - Dandy Business Park, Vejle, Denmark
Duration: 24. Aug 202225. Aug 2022
https://www.energyinformatics.academy/eia-2022-conference

Conference

ConferenceEnergy Informatics.Academy Conference 2022
LocationDandy Business Park
Country/TerritoryDenmark
CityVejle
Period24/08/202225/08/2022
Internet address

Keywords

  • Artificial neural network
  • Ecosystem
  • Feature identification
  • Feature selection
  • Recurrent neural network
  • Residential electricity consumption
  • Short-term load forecasting

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