Article
MONITORING OF INDIVIDUAL HOUSEHOLD ELECTRICAL APPLIANCES AND PREDICTION OF ENERGY CONSUMPTION USING MACHINE LEARNING
The paper proposes a scalable ensemble approach for forecasting the electricity consumption of households. SVM (support vector machine) based approach is a combination of different machine learning models, including linear regression, decision trees, and random forests, to improve the accuracy of the forecasts. The study was conducted using real-world data from households in the Netherlands, and the results show that the proposed approach outperforms traditional singlemodel approaches and achieves better accuracy in electricity consumption forecasting. The approach is scalable and can be applied to large datasets, making it suitable for use in real-world applications. SVM (Support vector machine) is a machine learning algorithm for load forecasting Long-term individual household forecasting may be used in a variety of applications, such as determining customer advance payments. Yet, there is a scarcity of literature on this form of forecasting current approaches either focus on short-term projections for individual families or long-term predictions at an aggregated level. To remedy this void, we describe a strategy that forecasts each monthly consumption over the future year using only a few months of consumption data from the current year. Utility providers may use this strategy to forecast any customer's use for the coming year even with limited data. Future forecasting of power consumption of linear regression for power consumption of data. Linear regression algorithm is implemented for future forecasting of data.
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