Short-Term Transformer Load Forecasting Based on Artificial Intelligence

In the era of big data, the total scale of data has increased significantly and the types are diversified. The traditional data analysis and processing technology has great limitations, resulting in low accuracy of data analysis and low processing efficiency. However, with the continuous improvement of smart grid construction, a large amount of power data accumulated in the power system needs to be analyzed efficiently and accurately. Based on the above problem background, this paper takes transformer load data as the research object, uses machine learning and other related technologies to study the law of transformer load curve changes, analyzes the similarity of power consumption behavior among transformers, and studies the distribution transformer short-term load prediction method, which helps to carry out load prediction, tariff design and other big data research related to the power industry, and also provides the realization of smart grid and efficient energy utilization provide important support. Python demonstrates the correctness and feasibility of the transformer power prediction method.