Paper Info
Title
An artificial neural network-based forecasting model of energy-related time series for electrical grid management
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.
Year
DOI
Venue
2021
10.1016/j.matcom.2020.05.010
Mathematics and Computers in Simulation
Keywords
DocType
Volume
Modeling,Artificial neural network,Solar radiation,Wind speed,Grid management
Journal
184
ISSN
Citations 
PageRank 
0378-4754
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
A. Di Piazza100.34
Maria Carmela Di Piazza2397.44
G. Tona372.24
Luna, M.4103.26