Deep Recurrent Neural Network-Based Forecasting of Electricity Consumption and Anomalies Detection

The construction industry is among the greatest fuel consumers of the world and a major source of carbon dioxide. Owing to this, the environmental effect can be minimized when focusing on conserving energy in buildings. Misuse of energy in the effort of equipment and errors of humans in the work is never considered budget. The use of smart buildings will address this problem since it will track the use of energy, detect abnormal behavior, and remind the managers that they are supposed to take energy-conservation actions. The current paper considers the issue of anomaly detection in the hourly electricity consumption level on a real basis and gives a two-step process with a Long Short-Term Memory (LSTM) network. In the first step, there will be forecasting of energy consumption, and, following this, the anomalies will be identified with the assistance of an LSTM Autoencoder. The article draws comparisons between highly complex time-dependent feature extraction algorithms like Rough Autoencoder (RAE), Deep Temporal Dictionary Learning (DTDL). The other algorithms could not perform better than the proposed method, the range of R-squared value was 95.11, MAE was 38.5, the MSE was 2448.94, and the RMSE was 49.49. Besides, the paper evaluates the means through which the AI-based anomaly detection solutions can provide forecasts of the electricity consumption, and the LSTM networks and autoencoders were tested to be more appropriate in forecasting the electricity consumption than the other deep learning algorithms.