Short-term mixed electricity demand and price forecasting using adaptive autoregressive moving average and functional link neural network

• Published in: Journal of modern power systems and clean energy Vol. 7; no. 5; pp. 1241 - 1255
• Main Authors: DASH, Sujit Kumar, DASH, Pradipta Kishore
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE
• Subjects: Adaptive filters; Autoregressive moving average; Basis functions; Cubature Kalman filter; Economic forecasting; Electric power demand; Electrical Machines and Networks; Electricity consumption; Electricity pricing; Energy; Energy Systems; Kalman filters; Mixed demand and price forecasting; Neural networks; Nonlinearity; Performance measurement; Power Electronics; Price spikes; Renewable and Green Energy; Smart grid; Smart grids; Cubature Kalman filter; Smart grids; Mixed demand and price forecasting; Price spikes

A new hybrid adaptive autoregressive moving average (ARMA) and functional link neural network (FLNN) trained by adaptive cubature Kalman filter (ACKF) is presented in this paper for forecasting day-ahead mixed short-term demand and electricity prices in smart grids. The hybrid forecasting framework...