Research and application of a combined model based on multi-objective optimization for electrical load forecasting
| Autor: | Congjun Jin, Meng-Xia Yu, Jing Ma, Liye Xiao, Wei Shao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Engineering Optimization algorithm Electrical load business.industry 020209 energy Mechanical Engineering Load forecasting Stability (learning theory) 02 engineering and technology Building and Construction Pollution Multi-objective optimization Industrial and Manufacturing Engineering Hilbert–Huang transform General Energy 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data pre-processing State (computer science) Electrical and Electronic Engineering business Simulation Civil and Structural Engineering |
| Zdroj: | Energy. 119:1057-1074 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2016.11.035 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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