Short-Term Electricity Price Forecasting with Stacked Denoising Autoencoders

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)2673-2681
Journal / PublicationIEEE Transactions on Power Systems
Issue number4
Online published15 Nov 2016
Publication statusPublished - Jul 2017


A short-term forecasting of the electricity price with data-driven algorithms is studied in this research. A Stacked Denoising Autoencoder (SDA) model, a class of deep Neural Networks (DNN), and its extended version are utilized to forecast the electricity price hourly. Data collected in Nebraska, Arkansas, Louisiana, Texas, and Indiana hubs in U.S. are utilized. Two types of forecasting, the online hourly forecasting and day-ahead hourly forecasting, are examined. In on-line forecasting, SDA models are compared with data-driven approaches including the classical neural networks (NN), support vector machine (SVM), multivariate adaptive regression splines (MARS), and least absolute shrinkage and selection operator (Lasso). In day-ahead forecasting, the effectiveness of SDA models is further validated through comparing with industrial results and a recently reported method. Computational results demonstrate that SDA models are capable to accurately forecast electricity prices and the extended SDA model further improves the forecasting performance.

Research Area(s)

  • Comparative analysis, data mining, electricity price, hourly forecasting, neural networks