A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

Andrés M. Alonso, Francisco J. Nogales and Carlos Ruiz
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Andrés M. Alonso: Department of Statistics, Universidad Carlos III de Madrid, 126-28903 Getafe, Spain
Francisco J. Nogales: Department of Statistics, Universidad Carlos III de Madrid, 126-28903 Getafe, Spain
Carlos Ruiz: Department of Statistics, Universidad Carlos III de Madrid, 126-28903 Getafe, Spain

Energies, 2020, vol. 13, issue 20, 1-19

Abstract: Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.

Keywords: load forecasting; disaggregated time series; neural networks; smart meters (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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