Regression Prediction of Smart Home Power Consumption Based on Machine Learning Algorithms

With the acceleration of the global energy transition process and the wide popularization of smart homes, household electricity consumption data is showing high-frequency and multi-dimensional characteristics. Hourly electricity consumption prediction has gradually become a key technical link to support the load dispatching of smart grids and help users achieve energy-saving management. Machine learning algorithms, with their powerful nonlinear fitting capabilities and efficient feature learning abilities, provide effective solutions for temporal power consumption prediction. This paper proposes the HLOA-CNN-BIGRU regression algorithm. Firstly, correlation analysis and violin plot analysis are carried out. Then, through comparative experiments of multiple machine learning algorithms, it is found that the KELM-LSTM-Transformer algorithm shows significant advantages in all indicators: Its MSE is 0.002, which is much lower than the minimum value of 0.006 in other algorithms. The RMSE is 0.047, significantly lower than the minimum value of 0.075 in other algorithms. The MAE is 0.038, which is significantly better than the minimum value of 0.056 in other algorithms. The MAPE is 2.28%, which is lower than the minimum value of 3.49% in other algorithms. R² is 99.4%, which is higher than the maximum value of 98.7% in other algorithms. Among traditional algorithms, ExtraTrees has a relatively superior overall performance, with R² reaching 98.7%, RMSE 0.075, and MAPE 3.58%, outperforming algorithms such as AdBoost and Random Forest. The overall performance of GBDT is relatively weak, with R² being only 96.8%, and MSE, RMSE, MAE, and MAPE are all the highest among all traditional algorithms. The R² of CatBoost is 97.8%, and its various error indicators are also higher than those of algorithms such as ExtraTrees and AdBoost. Overall, the KELM-LSTM-Transformer algorithm comprehensively outperforms other compared machine learning algorithms in terms of prediction accuracy and fitting effect, with stronger performance, providing important technical support for the efficient operation of smart grids and the refined management of household energy.