Intelligent Forecasting of Smart Home Energy Consumption and Generation Based on Weather Variables

Abstract

The growing adoption of artificial intelligence (AI) aims to improve global standards of living. However, this trend is accompanied by an increasing demand for energy. AI is rapidly integrating into home energy management systems (HEMS). While numerous artificial neural network (ANN) models have been developed for energy consumption forecasting, they often require significant computational resources and expertise for both development and deployment. This study presents a user-friendly engineering methodology for designing accurate multilayer perceptron (MLP) and Cascade-Forward Network (CFN) models to predict smart home energy consumption and solar generation. The approach emphasises computational simplicity and widespread practical viability. The dataset of 8399 hourly recorded weather and energy variables over the course of a year is extracted from publicly available data. It is randomly split into 70% for training, 15% for validation and 15% for testing. A proposed methodology guides the design, training, validation, and testing of various CFN-MLP models, in which weather variables with the greatest impact on energy generation and consumption are selected for model inputs based on different correlation tests. The identified optimal models each have two weather variables as inputs and two hidden layers of 8 and 16 neurons, balancing high predictive accuracy with low computational load for real-time HEMS deployment. The high prediction accuracy is supported by the small mean squared errors of 0.76 kWh and 0.0046 kWh. The models’ feasibility is facilitated by simple architectures, which demand only two smart sensors for the inputs, are affordable by most homes, and require small memory storage for the models.