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RESEARCH PRODUCT

An efficient data model for energy prediction using wireless sensors

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International audience; Energy prediction is in high importance for smart homes and smart cities, since it helps reduce power consumption and provides better energy and cost savings. Many algorithms have been used for predicting energy consumption using data collected from Internet of Things (IoT) devices and wireless sensors. In this paper, we propose a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information (e.g., light energy, day of the week, humidity, temperature, etc.) from a Wireless Sensor Network (WSN). We compare our system against four other classification algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM) and Random Forest (RF). We achieve state-of-the-art results with 64% of the coefficient of Determination R2, 59.84% Root Mean Square Error (RMSE), 27.28% Mean Absolute Error (MAE) and 27.09% Mean Absolute Percentage Error (MAPE) in the testing set when using weather and temporal data.