Urban Annual Electricity Consumption Prediction Method Based On Fuzzy Cognitive Map

Electricity has become an indispensable necessity for modern human society, and electricity consumption prediction is a necessary guarantee for stable social operation and healthy industrial development. In recent years, experts and scholars have put forward many models of electricity consumption prediction, but when considering complex systems and non-linear relations, there is no good explanation, and the prediction accuracy will be affected. Fuzzy Cognitive Map (FCM) is a soft computing method, which is the product of the combination of fuzzy logic and neural network. It has a strong ability of knowledge expression and reasoning in complex system modeling. In this paper, a method based on FCM is proposed to predict the annual electricity demand of the city. Taking the economy, industrial structure, population, climate, urban development and other factors into consideration, the electricity consumption of Jinan city is predicted. Experiments show that compared with traditional forecasting methods and neural network algorithms, the prediction accuracy is better, which can provide more accurate reference data for government decision-making, industrial development planning and other aspects.