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
• ISSN: 2196-5625; 2196-5420
• DOI: 10.1007/s40565-018-0496-z

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 is intended to capture the dynamic interaction between the electricity consumers and the forecasted prices resulting in the shift of demand curve in electricity market. The proposed model comprises a linear ARMA-FLNN obtained by using a nonlinear expansion of the weighted inputs. The nonlinear functional block helps introduce nonlinearity by expanding the input space to higher dimensional space through basis functions. To train the ARMA-FLNN, an ACKF is used to obtain faster convergence and higher forecasting accuracy. The proposed method is tested on several electricity markets, and the performance metrics such as the mean average percentage error (MAPE), and error variance are compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability for a real-time forecasting.