Urban Electric Load Forecasting Using Combined Cellular Automata

Abstract

With the high-speed economic development in China, the transition of structural function in the urban land system highly effects the development of the urban electric load. Forecasting the urban electric load accurately is the foundation of decision making scientifically for the development and planning of the urban power grid in China. This paper improves the decision method of Transition Matrices of Land Use and Cover Change though integrating Cellular Automation with Markov Model firstly. Then, the combined cellular automation model is used to simulate the urban land function evolvement and forecast the land functions in the future as the start point for electric load forecasting. Considering the changes of urban land functions, electric load density and simultaneity factor, the urban electric load forecasting model is proposed. The model validation is performed by comparing model predictions with the load data and error analysis of different load forecasting methods though case study. The results obtained bear out the accuracy of the adopted methodology for urban load forecasting. Finally, some reasonable suggestions for the improvement of the forecast are given and the future work is raised.