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has been cited by the following article:

• TITLE: Monthly Electricity Consumption Forecast Based on Multi-Target Regression

  AUTHORS: Haiming Li, Ping Chen

  KEYWORDS: Forecasting, Multi-Target Tree Regression, Electricity, Monthly Electricity Consumption, Predict

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.7, July 24, 2019

  ABSTRACT: Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.