Hybrid Data Mining and MSVM for Short Term Load Forecasting

• Main Authors: Ren-fu Yang, 楊仁富
• Other Authors: Whei-Min Lin
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/28661440234858506249

碩士 === 國立中山大學 === 電機工程學系研究所 === 98 === The accuracy of load forecast has a significant impact for power companies on executing the plan of power development, reducing operating costs and providing reliable power to the client. Short-term load forecasting is to forecast load demand for the duration of one hour or less. This study presents a new approach to process load forecasting. A Support Vector Machine (SVM) was used for the initial load estimation. Particle Swarm Optimization (PSO) was then adopted to search for optimal parameters for the SVM. In doing the load forecast, training data is the most important factor to affect the calculation time. Using more data for model training should provide a better forecast results, but it needs more computing time and is less efficient. Applications of data mining can provide means to reduce the data requirement and the computing time. The proposed Modified Support Vector Machines approach can be proved to provide a more accurate load forecasting.