An artificial neural network-based forecasting model of energy-related time series for electrical grid management

A. Di Piazza, M.C. Di Piazza, G. La Tona and M. Luna

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 184, issue C, 294-305

Abstract: Forecasting of energy-related variables is crucial for accurate planning and management of electrical power grids, aiming at improving overall efficiency and performance. In this paper, an artificial neural network (ANN)-based model is investigated for short-term forecasting of the hourly wind speed, solar radiation, and electrical power demand. Specifically, the non-linear autoregressive network with exogenous inputs (NARX) ANN is considered, compared to other models, and then selected to perform multi-step-ahead forecasting. Different time horizons have been considered in the range between 8 and 24 h ahead. The simulation analysis has put in evidence the main advantage of the proposed method, i.e., its capability to reconcile good forecasting performance in the short-term time horizon with a very simple network structure, which is potentially implementable on a low-cost processing platform.

Keywords: Modeling; Artificial neural network; Solar radiation; Wind speed; Grid management (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:184:y:2021:i:c:p:294-305

DOI: 10.1016/j.matcom.2020.05.010

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