A Multi-Dimensional Method for Nodal Load Forecasting

Abstract:

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

• Periodical: Advanced Materials Research (Volumes 1070-1072)
• Pages: 708-717
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.708
• Online since: December 2014
• Authors: Zhi Yuan Pan*, Chao Nan Liu, Jing Wang, Yong Wang
• Keywords: Approximate Optimal Prediction, Basic Forecasting Unit, Combination Forecasting, Energy Flow Topology, Kalman Filtering, Multi-Dimensional, Nodal Load Forecasting, Support Vector Machine (SVM)

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