Paper Info
Title
Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition.
Abstract
In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
Year
DOI
Venue
2015
10.1109/ACCESS.2015.2485943
IEEE ACCESS
Keywords
Field
DocType
Time-series,forecasting,electric load prediction,echo state network,genetic algorithm,PCA,dimensionality reduction,smart grid
Time series,Orthogonal transformation,Electrical load,Computer science,Electric power distribution,Artificial intelligence,Distributed computing,Load management,Algorithm,Autoregressive integrated moving average,Curse of dimensionality,Echo state network,Machine learning
Journal
Volume
ISSN
Citations 
3
2169-3536
15
PageRank 
References 
Authors
0.69
21
4
Name
Order
Citations
PageRank
Filippo Maria Bianchi116015.76
Enrico De Santis2505.92
Antonello Rizzi336341.68
Alireza Sadeghian426925.59