An artificial neural network-based forecasting model of energy-related time series for electrical grid management
| Autor: | Massimiliano Luna, A. Di Piazza, M. C. Di Piazza, G. La Tona |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science solar radiation Time horizon 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Wind speed Theoretical Computer Science 0202 electrical engineering electronic engineering information engineering 0101 mathematics grid management Numerical Analysis Nonlinear autoregressive exogenous model Artificial neural network Applied Mathematics Control engineering modeling Electrical grid Autoregressive model Modeling and Simulation 020201 artificial intelligence & image processing Electric power wind speed Energy (signal processing) artificial neural network |
| Zdroj: | Mathematics and computers in simulation 184 (2021): 294–305. doi:10.1016/j.matcom.2020.05.010 info:cnr-pdr/source/autori:Di Piazza A.; Di Piazza M.C.; La Tona G.; Luna M./titolo:An artificial neural network-based forecasting model of energy-related time series for electrical grid management/doi:10.1016%2Fj.matcom.2020.05.010/rivista:Mathematics and computers in simulation (Print)/anno:2021/pagina_da:294/pagina_a:305/intervallo_pagine:294–305/volume:184 |
| DOI: | 10.1016/j.matcom.2020.05.010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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