Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders

• Published in: IEEE transactions on power systems Vol. 32; no. 4; pp. 2673 - 2681
• Main Authors: Wang, Long, Zhang, Zijun, Chen, Jieqiu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Comparative analysis; data mining; Data models; Electric utilities; electricity price; Electricity pricing; Forecasting; hourly forecasting; Hubs; Mathematical models; Neural networks; Noise reduction; Predictive models; Splines; Wind forecasting

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, and its extended version are utilized to forecast the electricity price hourly. Data collected in Nebraska, Arkansa...