Research on Home Electricity Prediction Based on LSTM

This article proposes a deep learning model based on Long Short Term Memory (LSTM) network for multivariate time series prediction of household electricity consumption. The study is based on a household electricity dataset that includes global active power, voltage, current, and sub meter readings. Missing values are processed through mean interpolation, and time series continuity is optimized through hourly granularity resampling. Normalize 7 power features using MinMaxScaler, construct a sliding window supervised learning structure, and use historical time step features as input to predict the global active power (Global'active_power) at the current time step. The model adopts a single-layer LSTM unit (100 neurons) combined with Dropout regularization (ratio 0.1) to prevent overfitting, and is trained with mean square error (MSE) as the loss function and Adam as the optimizer. The experiment used the first 4000 hours of data as the training set and subsequent data as the testing set. After 100 rounds of training, the model achieved a prediction accuracy of RMSE [insert specific value] on the testing set. The visual comparison shows that the predicted values are highly consistent with the actual values in the first 500 hours. The results indicate that LSTM networks can effectively capture the complex temporal patterns of household electricity consumption, providing a reliable technical path for load forecasting and energy management in smart grids. This study further revealed the periodic patterns of electricity consumption through multi-scale data resampling (monthly, daily, hourly), providing data support for the interpretability of the model.