Abstract
A short-term load forecasting method considering meteorological factors and electric vehicles is essential to the successful operation of the power system. This paper proposes a unique short-term load forecasting method based on neural network. First, through the analysis of typical daily load data, it is demonstrated that the short-term load data changes with the daily, weekly, weather type and the charging of electric vehicles. Then, the load forecasting model based on the neural network is set up with historical data, meteorological data and electric vehicle charging data as input. Finally, the prediction model is simulated to improve the accuracy of load forecasting.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.