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• W4387142139 abstract "Accurate load forecasting is important to mitigate the negative impact of Electric vehicle integration into the existing grid. Previous studies mostly focus on individual or aggregated levels without specifying the impact of accuracy due to the selection of different spatial levels and lack the integration of uncertainty estimation in the forecasting models. To address these issues, this study compares the predictive performance of a Random Forest and Artificial Neural Networks at different spatial levels with 15-min resolution data across case studies (i) with 2 Electric Vehicles charging poles and 3 users, (ii) with 75 charging poles, 8 charging rails and 70 users. The outcome shows that forecasting the Electric Vehicle load of smaller case studies will require the presence or calendar information of users. Whereas in case studies with more than 10 charging piles, the features “previous week's power”, “hour of the day” and the “number of connections” can achieve similar results. The results also showed that the aggregated forecasting was more accurate than individual charging piles. Moreover, the uncertainty plot generated for a 90% prediction interval showed that the uncertainty estimates were more reliable for the case study with large numbers of Electric Vehicles." @default.
• W4387142139 created "2023-09-29" @default.
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• W4387142139 date "2023-11-01" @default.
• W4387142139 modified "2023-10-15" @default.
• W4387142139 title "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation" @default.
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• W4387142139 doi "https://doi.org/10.1016/j.energy.2023.129213" @default.
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