Optimizing heating operation via GA- and ANN-based model predictive control: Concept for a real nearly-zero energy building

Using a simulation-and optimization-based framework that leverages artificial neural networks for the model predictive control of space heating operation, machine learning techniques are explored to predict the energy performance of a real nearly zero energy building located in Benevento (Southern Italy, Mediterranean climate). The framework's goal is to minimize heating energy costs and thermal discomfort by providing optimal values of setpoint temperatures on a day-ahead planning horizon based on weather forecasts. To achieve this, a Pareto multi-objective approach is applied, which considers thermal comfort via the adaptive theory of ASHRAE 55, assessing a comfort penalty function. The optimization problem is solved using a genetic algorithm in the MATLAB (R) environment. Objective functions are evaluated using the coupling between MATLAB (R) and Ener-gyPlus, which is used as building performance simulation tool. Multi-criteria decision-making is performed to select an optimal solution from the Pareto front while setting a limit to the comfort penalty. To test the framework, EnergyPlus weather data is used to simulate weather forecasts for typical days of the winter season. Two different economic scenarios are simulated in order to be able to analyze both conditions and given the variability of the electricity price. For the different days tested, the best results in terms of savings achieved through the proposed solution are on Feb. 28, corresponding to the coldest day and the highest energy cost, with daily savings of 26% compared to a reference control with a fixed setpoint of 21 degrees C, while maintaining similar comfort performance. The results suggest that optimized control is a crucial aspect of sustainable building design, and the proposed framework can be virtually implemented or integrated into automation systems for real-time model predictive control.