Abstract
In Smart Grid, electricity demand and price forecasting literature has focused on Industrial, Buildings, and Residential sector demand, but this paper focuses on short term electricity demand and price forecasting for residential customer. Here we take smart meter data of hourly based from a smart home. First standardize and selected important features by using Recursive Feature Elimination with Linear Support Vector Classifier (RFE-LSVC). Second, do forecasting through K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Regression (SVR) models and perform comparative analysis among models against four scenarios and provided best solution among all for individual scenario. This work proposed best solution of smart home’s load and price forecasting for smart grid to manage demand response efficiently. We evaluated every Models with Mean Absolute Percentage Error (MAPE).
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Notes
- 1.
Smart* Dataset is taken from UMass Smart Repository. The Goal of Smart* project is to optimize home energy consumption. http://traces.cs.umass.edu/index.php/Smart/Smart.
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Nawaz, M. et al. (2020). An Approximate Forecasting of Electricity Load and Price of a Smart Home Using Nearest Neighbor. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_46
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