Abstract
In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of Beijing from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system. The fuzzy probability Markov chain model is helpful for regional electricity power system managers in not only predicting a long term power load under uncertainty but also providing a basis for making multi-scenarios power generation/development plans.
Highlights
Electric power is one of the most usual and important energy in human life and economic development
The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals
Higher satisfaction degree denotes that the real transition probability close to the probability generated from the statistic data; and the lower satisfaction degree indicates that the real transition probability distributes is in a wide range around the probability gotten from statistic data
Summary
Electric power is one of the most usual and important energy in human life and economic development. There are many uncertain and limited factors that exist in many system parameters and their interrelationships, including the influence of weather, nature environment, human activities and economic structural adjustment All of those would lead to the uncertainty in long term power load forecasting and the versatility of electric power demand in future. A variety of forecasting models of electric power load have been developed, such as grey Markov model[1], artificial neutral model[2], recurrent support vector machines with genetic algorithms (RSVMG) model[3], hybrid ellipsoidal fuzzy systems (HEFST) [4] and adaptive network based fuzzy inference system (ANFIS) [5] All of these models attempt to minimize the forecasting error, but have less ability to reflect different possible of power demand caused by uncertainty, especially the economic structural adjustment in developing countries (e.g. China). The power load of Beijing is forecasted in different satisfaction degrees
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