Abstract
Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the gray correlation analysis method is used to screen the important factors, and three factors with large correlation degree are selected as the input parameters of BP neural network. The power forecasting model uses nearly 18 years of data to train and validate the model. The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction, and the calculation is more convenient than traditional methods.
Highlights
The regional power grid electricity consumption forecast refers to the demand for electric energy from local individuals and business users
Based on the research on historical data, it makes a reasonable estimate of the regional electricity consumption, so that the relevant departments can work in the formulation and Policy reference
The rest of this paper is organized as follows: In Section 2, we analyze and determine Determination of factors affecting electricity consumption based on grey correlation theory, and a regional power consumption prediction training method based on BP neural network is proposed, while an example experimental is performed to show its feasibly
Summary
The regional power grid electricity consumption forecast refers to the demand for electric energy from local individuals and business users. Accurate power load forecasting helps grid companies to establish appropriate operational practices and bidding strategies and is an important basis for developing power development plans. Accurate electricity consumption forecasting has important reference value for rational design of power grid transformation, peak power consumption, and power generation planning. It is of great significance for the establishment of energy-saving society and industrial policies, and contributes to. The rest of this paper is organized as follows: In Section 2, we analyze and determine Determination of factors affecting electricity consumption based on grey correlation theory, and a regional power consumption prediction training method based on BP neural network is proposed, while an example experimental is performed to show its feasibly. In the last Section, conclusion and discussion are conducted
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