| The power industry is the foundation of the energy industries field. How to get the rapid development and the healthy of power industry at the same time is becoming a important problem. For the power load forecasting theory and technology, it is of great significance for optimizing the power generation plan, making the power deployment plan and making electricity price biding plan when the power load forecasting accuracy improved, it also have direct and significant economic and social benefits. Because the period of short-term power load forecasting is very short, so it is of great significant for arranging the day off on plan and making the power generation plan. The role of short-term load forecasting depends on the level of the forecasting accuracy, so it's the key point to study and improve the accuracy in the current time. Domestic and foreign researchers study short-term load forecasting more than long-term forecasting.On the other hand, with the development of smart distribution grid, the grid will gradually add the smart units, such as variety of distributed power resource, electric vehicle charging station, charging pile and smart electricity-consumption living area, etc. The adoption of these smart units will bring obviously impact to the load model and the load growth model, making the accuracy of conventional forecasting methods can not meet the requirements. To address the situation this paper presents a new method: splitting and modeling. It means split the normal part and the smart part of the grid first, then modeling and forecasting the separated parts. At last, combine each forecasting results parts together, you can get the entire distribution grid power load forecasting results.This paper adopt the artificial neural network and time series method to study the short-term power forecasting; in the smart distribution grid aspect, the paper focus on the wind power, photovoltaic generation and electric vehicle charging station, reaching the power output of them. |
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