Abstract
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.
Highlights
Since the second industrial revolution, the steady supply of electricity load is a basic requirement for maintaining the normal functioning of modern society
The The comparison between the predict values and the actual values of the four models is given by the Figures 7 and 8, Figure 7 demonstrates the results in the testing stage, and Figure 8 illustrates the the Figure 7 and Figure 8, Figure 7 demonstrates the results in the testing stage, and Figure 8 results in training stage, where the REP denotes real error points
The dramatic increase of electric vehicles (EVs) exerts significant pressures to the power system operators, and an accurate load forecasting model is a key solution to this problem
Summary
Since the second industrial revolution, the steady supply of electricity load is a basic requirement for maintaining the normal functioning of modern society. With the fast development of the EV industry, it is bound to bring new changes to the power field due to the large capacity of the battery and stochastic charging behaviors of the users. In this regard, accurate short-term load forecasting is a key measure to the intelligent control of EV charging systems. Given the complicated practical application, the dataset includes a number of data types, whereas only three featured data types are valuable for the forecasting model These specific types are charging time, charging quantity and real-time electricity price and have been adopted as input respectively. The quality of the raw data is low, and it is of importance for data pre-processing to the forecasting model
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