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- W4210273313 abstract "AbstractAs the world exhausts its non-renewable energy reservoirs, building predictive models for household energy consumption becomes important a fortiori. In this study, we examine how XGBOOST, LSTM, and CNN perform on big data trends in a time series forecasting. The data used incorporates 2,075,259 measurements that were gathered from a residence situated in Sceaux, which is 7 km from Paris, France. The performance has been calculated on the parameters of mean absolute error (MAE) and root mean square error (RMSE). The period of data taken for analysis is from December 2006 to November 2010, which sums up for 47 months. The dataset provides a measurement of power consumption in a single household with one minute of the sampling rate. Exploratory data analysis and statistical tests were performed to produce stationarity of the time series data. Predictions are made by identifying previously captured data and further processing null values, which could be used to forecast future circumstances. This can further be used to calculate the active power consumption of a household.KeywordsTime series forecastingXGBOOSTLSTMCNNStandard scalarMatplotlib" @default.
- W4210273313 created "2022-02-08" @default.
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- W4210273313 date "2022-01-01" @default.
- W4210273313 modified "2023-10-16" @default.
- W4210273313 title "Time Series Forecasting for Electricity Consumption Using ML and DL Algorithms" @default.
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- W4210273313 doi "https://doi.org/10.1007/978-981-16-7664-2_21" @default.
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