A Novel Approach for Short-Term Energy Forecasting in Smart Buildings

Efficient energy management is required for optimal energy consumption. The building sector consumes 40% of the total global energy production and is expected to reach 50% by 2050. With the soaring price of electricity, buildings need economical and efficient energy management. Recent advances in artificial intelligence and the Internet of Things (IoT) have inspired researchers working in smart building management to harness the potential of these technologies for forecasting energy consumption in smart buildings. This article proposes a novel hybrid deep learning model consisting of convolutional neural network (CNN) and recurrent neural network (RNN) to predict hourly energy consumption for smart buildings. Experimental results demonstrate that the CNN-gated recurrent unit (GRU) model, with an accuracy of 97%, outperforms the state-of-the-art techniques.

Automated summary

Efficient energy management is crucial for optimal energy consumption. The building sector currently accounts for 40% of total global energy production, a number expected to rise to 50% by 2050. This article proposes a hybrid deep learning model, combining convolutional neural network (CNN) and recurrent neural network (RNN), to accurately predict hourly energy consumption in smart buildings. Experimental results demonstrate that the CNN-gated recurrent unit (GRU) model outperforms state-of-the-art techniques with an accuracy of 97%.