Deep Learning-Based Prediction of Household Energy Consumption: An LSTM Approach with High Precision

Accurate forecasting of household electricity consumpation used for is crutial for demand management and smart grid operations. This study employs a Long Short-Term Memory (LSTM) network to predict electricity usage using four years of high-resolution energy data, including variables such as active and reactive power, voltage, and sub-metering values. preprocessing methods such as data smoothing, stationarity adjustments, and time-based feature extraction, was conducted to enhance model performance. The model demonstrated remarkable results with a training Mean Absolute Error (MAE) of 0.0074, Mean Squared Error (MSE) of 0.0102, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9999, alongside a testing MAE of 0.0092, MSE of 0.0123, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9998. These results demonstrate the model’s ability to accurately capture complex consumption patterns and provide reliable forecasts for energy management applications