Evaluation of neural networks for residential load forecasting and the impact of systematic feature identification

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 R2 score and 4–5% absolute percentage error for the 1-h predictions. Forecasting 24 h gave an adjusted R2 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.

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“…If there are numerous aggregators operating on the market, it is in the best interest of an electric vehicle owner to choose the aggregator that is the most suitable for their needs [101]. The aggregators, in conjunction with the DSO, make projections regarding the demand for energy for the following day and also calculate their purchase and sell rates [102]. As part of their duties, the DSO is responsible for conducting an analysis and evaluation of the technical viability of demand projections [103].…”

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“…The five-ecosystem CSTEP framework and evaluation of neural networks for residential load forecasting [7]: The forecasting of the power load in Danish residential areas is the main goal of this study. Recurrent neural networks (RNN), long-short-term memory networks (LSTM), gated recurrent units (GRU), and feed-forward networks (FFN) are just a few of the neural network types that are evaluated in this study.…”