RT Journal Article
T1 A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series
A1 Alonso Fernández, Andrés Modesto
A1 Nogales Martin, Fco. Javier
A1 Ruiz Mora, Carlos
AB 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.
PB MDPI
SN 1996-1073
YR 2020
FD 2020-10-02
LK https://hdl.handle.net/10016/33161
UL https://hdl.handle.net/10016/33161
LA eng
NO This article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence.
NO The authors gratefully acknowledge the financial support from the Spanish government through projects MTM2017-88979-P and PID2019-108311GB-I00/AEI/10.13039/501100011033, and from Fundación Iberdrola through “Ayudas a la Investigación en Energía y Medio Ambiente 2018”.
DS e-Archivo
RD 1 sept. 2024