An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting. (March 2021)

• Record Type: Journal Article
• Authors: Tian, Jing; Li, Kangji; Xue, Wenping
• Is Part Of: Sustainable cities and society. Volume 66(2021)
• Publication Date: 2021-03
• Subjects: Electrical energy prediction -- Ensemble learning -- Hybrid data-driven model -- Evolution algorithm -- Recursive feature elimination -- Fuzzy c-means clustering -- Multiple scales; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102654
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: An adaptive ensemble model strategy is proposed for multiple prediction scenarios. RFE and FCM are used for feature identification and data clustering. Key parameters of member predictors are optimized by swarm evolution algorithms. Four case studies are performed representing different prediction scenarios. Abstract: Electrical energy prediction plays an important role in energy management, power plant scheduling, peak demand and grid security conflict. To deal with prediction scenarios at multiple energy and time scales, an adaptive ensemble model strategy with wider applicability is proposed. The strategy consists of three parts. Firstly, the data preprocessing part is composed of expert knowledge, recursive feature elimination (RFE) and fuzzy c-means clustering (FCM). RFE and FCM methods are used for feature identification and data clustering for different energy usage patterns. Secondly, the primary prediction part consists of a model library containing five commonly used data-driven models. To improve their prediction accuracies, key model parameters are optimized by swarm evolution algorithms (EAs). An algorithm package containing three EAs is combined with the model library. The choice of algorithm for each model depends on the comparison of accuracy in specific prediction scenarios. In the third part, a linear regression model provides the final result based on primary predictors' outputs. Its weights are also optimized by EAs from the algorithm package. To verify the performance of this strategy, four case studies are carried out representing different energy and time scales' prediction scenarios. Case A is a benchmark case from the first energy prediction competition organized by American Society of Heating Refrigerating and Air-conditioning Engineer (ASHRAE). Case B perform hourly electrical load prediction of a whole building from University of Wyoming, USA. Case C is a city-scale daily electrical load forecasting (Yizheng City, Jiangsu Province, China), and in Case D, national wide monthly electrical load prediction of the United States is carried out. Results of the four cases indicate that: (1) The proposed ensemble strategy performs best in all four case studies compared with five primary predictors. Compared to the best primary predictors in four case studies, the accuracies of the proposed model increase by 3% to 17% (MAPE); (2) Compared with previous reported ensemble models, the accuracy of the proposed model also increase by 11% (MAPE). The adaptive prediction framework is applicable and has the potential to become a general predictive strategy for energy predictions.