Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of...

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Bibliographic Details
Published in:Mathematics and computers in simulation Vol. 224; pp. 63 - 75
Main Authors: La Tona, G., Luna, M., Di Piazza, M.C.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2024
Subjects:
Energy management
Forecasting
LSTM
Residential electrical consumption
Residential electrical consumption
LSTM
Forecasting
Energy management
ISSN:0378-4754, 1872-7166
Online Access:Get full text
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Summary:Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.
ISSN:0378-4754
1872-7166
DOI:10.1016/j.matcom.2023.06.017