Application of relevance vector regression model based on sparse bayesian learning to long-term electricity demand forecasting

作者：Lin Niu, Jianguo Zhao, Min Liu · 年份：2009 · DOI：10.1109/icma.2009.5246213 · 被引用次数：7 · 研究领域：Energy Load and Power Forecasting、Grey System Theory Applications、Smart Grid and Power Systems

In electric power system, long term peak load forecasting plays an important role in terms of policy planning and budget allocation. The planning of power system expansion project starts with the forecasting of anticipated load requirement. Accurate forecasting method can be helpful in developing power supply strategy and development plan, especially for developing countries where the demand is increased with dynamic and high growth rate. This paper proposes a peak load forecasting model using relevance vector machine (RVM), which is based on a probabilistic Bayesian learning framework with an appropriate prior that results in a sparse representation. The most compelling feature of the RVM is, while capable of generalization performance comparable to an equivalent support vector machine (SVM), that it typically utilizes dramatically fewer kernel functions. The proposed method has been tested on a practical power system, and the result indicates the effectiveness of such forecasting model.