Research and application of a combined model based on multi-objective optimization for electrical load forecasting. (15th January 2017)

• Record Type: Journal Article
• Authors: Xiao, Liye; Shao, Wei; Yu, Mengxia; Ma, Jing; Jin, Congjun
• Is Part Of: Energy. Volume 119(2017)
• Page Start: 1057
• Page End: 1074
• Publication Date: 2017-01-15
• Subjects: Multi-objective flower pollination algorithm -- Short-term load forecasting -- Combined model -- Forecasting accuracy and stability -- Weight coefficient optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.11.035
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management of electrical systems but remains an extremely challenging task. To achieve the goal of load forecasting with both accuracy and stability, a combined model based on a multi-objective optimization algorithm, the multi-objective flower pollination algorithm (MOFPA), is developed in this study. In this combined model, MOPFA is used to optimize the weights of single models to simultaneously obtain high accuracy and great stability, which are two mostly independent objectives and are equally important to the model effectiveness. Data preprocessing techniques, such as the fast ensemble empirical mode decomposition and multiple seasonal patterns, are also incorporated in this model. Case studies of half-hourly electrical load data from the State of Victoria, the State of Queensland, and New South Wales, Australia, are considered as illustrative examples to evaluate the effectiveness and efficiency of the developed combined model. The experimental results clearly show that both the accuracy and stability of the combined model are superior to those of the single models. Highlights: Simultaneously improve the accuracy and stability of electrical load forecasting. Propose a combined model based on several artificial neural networks. Optimize weight coefficients of single models based on a multi-objective optimization algorithm. Use the multiple seasonal patterns and the fast ensemble empirical mode decomposition to pre-process data.