Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.

Most electric energy consumption in the building sector is provided by fossil fuels, leading to high greenhouse gas emissions. However, the increasing need for sustainable infrastructure has triggered a significant trend toward smart buildings, which enable optimal and efficient resource usage. In this context, accurate mid-term energy load forecasting is crucial for energy management. This study proposes a hybrid forecasting model obtained through the combination of machine learning (ML) and deep learning (DL) approaches designed to enhance forecasting accuracy at an hourly granularity. The hybrid two-layer architecture first investigates the model’s performance individually, such as decision tree (DT), random forest (RF), support vector regression (SVR), Extreme Gradient Boosting (XGBoost), FireNet, and long short-term memory (LSTM), and then combines them to leverage their complementary strengths in a two-layer hybrid design. The performance of these models is assessed on smart building energy datasets with weather data, and their accuracy is measured through performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared ($R^2$). The collected results show that the XGBoost outperformed other ML models. However, the hybrid model obtained by combining FireNet and XGBoost models delivers the highest overall accuracy for the performance parameters. These findings highlight the effectiveness of hybrid models in terms of prediction accuracy. This research contributes to reliable energy forecasting and supports environmentally sustainable practices.