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- W4382996863 abstract "Accurate electricity consumption forecasting has significant importance for the performance of power plant units and for generation costs. Thus, the need to use technique to predict the future power load has become important. The goal of this paper is to predict electricity consumption for different periods of time on a daily basis. A dataset of daily recorded energy demand of Spain from year 2014 to 2018 has been used for building a model by using RF algorithm. An evaluation of the dataset was made by calculating some statistical values. Correlation matrix measures the linear relationships between the newly created columns and the energy recorded in the next day. After fitting the proposed model, it will predict different future values for periods up to 30 days. Finally, a comparison was made between predicted values and actual data for assessing the performance of the model." @default.
- W4382996863 created "2023-07-04" @default.
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- W4382996863 date "2023-01-01" @default.
- W4382996863 modified "2023-09-25" @default.
- W4382996863 title "Applied Time Series Regression by Using Random Forest Algorithm for Forecasting of Electricity Consumption on a Daily Basis" @default.
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- W4382996863 doi "https://doi.org/10.1007/978-3-031-30171-1_22" @default.
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