Affiliations: [a]
School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China
| [b]
Institute of Marine Economics and Management, Shandong University of Finance and Economics, Jinan, China
| [c]
School of Insurance, Shandong University of Finance and Economics, Jinan, China
Correspondence:
[*]
Corresponding author. Kai Zhang, School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China. E-mail: kaizhanggg@163.com.
Abstract: Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting.
Keywords: Short-term load prediction, data selection, data preprocessing, optimization, forecasting
