A novel integrated price and load forecasting method in smart grid environment based on multi-level structure. (October 2020)

• Record Type: Journal Article
• Authors: Zhang, Yang; Deng, Caibo; Zhao, Ran; leto, Sebastian
• Is Part Of: Engineering applications of artificial intelligence. Volume 95(2020)
• Publication Date: 2020-10
• Subjects: MIMO model -- Smart grids -- DWT -- Feature selection -- Load and price prediction -- LSSVM; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103852
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Prediction of load and price are two critical key in power system planning and operation. Most of the recent works in this area forecast the load and price signals separately but, a dynamic model in smart grid is evaluated while, the customers may have opportunity to react the proposed prices changing through shifting the electricity usages from expensive to cheaper hours. So, the load and price signals are coupled strongly which made the previous prediction models ineffective. In this research, a synthetic prediction approach has been proposed by considering the load and price signals, simultaneously. This method works as multi-input multi-output (MIMO) model based on least square support vector machine (LSSVM) forecast engine. Furthermore, a dyadic wavelet transform (DWT) is suggested in this approach to decompose the original signal into different small sub-signals. Beside of that, the modified mutual information (MMI) filter has been used to choose the best candidate input of forecast engine. The learning section is also coupled with novel modified optimization algorithm based on gravitational search algorithm (GSA) which called as modified GSA (MGSA). Finally, various forecasting errors have been considered as average mean absolute percentage error and error variance to get the comparison outcomes and performance of forecasting approaches. For this purpose, different markets have been considered as test case to show the efficiency of suggested approach.