Electric vehicle charging load prediction based on demand behavior analysis and random forest algorithm

The lawsuiting requirement of galvanic carriage users is affected by a variety of factors, including working days, seasons, weather conditions, user journeys, etc. These factors make the lawsuiting mode and time of galvanic carriages diverse and uncertain, making load prognosis difficult. Therefore, this article proposes an EV lawsuiting load forecast measure based on requirement comportment analysis and random forest algorithm. K-mean clustering is applied to analyze the comportment impress of lawsuiting load requirement. Because the driving of galvanic carriages will be affected by weather, road conditions and other factors, it is difficult to predict. Thus, it is imperative to recover the mixed impress in the traffic sequence. Based on this, the random forest blueprint is constructed to classify the lawsuiting load and complete the prognosis of the lawsuiting load. The experimental results show that the load forecast results of galvanic carriages have a lofty extent of fitting with the tangible value. The time is shorter and the application is better.