Cyber Swarm Algorithm Based Multiple Prediction Methods for Taiwan Electricity Demand

• Main Authors: CHAO,CHING-HUI, 趙景暉
• Other Authors: Yin,Peng-Yeng
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/80321751010797910367

碩士 === 國立暨南國際大學 === 資訊管理學系 === 104 === Stable electricity supply is the basis of economic development for many countries. In order to satisfy the electric power demand of Taiwan's future economic development and environmental protection, new power plants using green energy must be developed, especial the potential solar and wind energy sources in off-island areas. Accurate electricity demand forecasting plays an important role in the economic dispatch of electricity system. This study develops three demand forecasting models based on the Cyber Swarm Algorithm (CSA) which inherits the main features from the particle swarm optimization algorithm. Because of its prevailing capability such as large searching range, few parameters, and fast convergence speed, the particle swarm optimization algorithm usually can quickly find a near optimal solution in the solution space. Moreover, CSA employs the search strategies introduced in reference set and path relinking in order to escape from the barrier of local optima. This study applies parametric and non-parametric regression models to process electricity demand data. Parametric models use important indicators for predicting electricity demand, while non-parametric models use Gauss functions to approximate historical data and shift the learned functions to predict future electricity demand. Finally, we propose a reinforcement learning model to integrate parametric and non-parametric models and obtain a better prediction result.