Several faults affect heating, ventilation, and air conditioning (HVAC) chiller systems, leading to energy wastage, discomfort for the users, shorter equipment life, and system unreliability. Early detection of anomalies can prevent further deterioration of the chiller and energy wastage. In this work, a data-driven approach is used in order to detect faults that usually plague chiller systems. In particular, the proposed approach employs a kernel principal component analysis (KPCA) in order to capture the normal operative conditions of the system; the learning method turns out to be effective in handling nonlinear phenomena through the use of the Gaussian kernel, which, by means of a self-tuning procedure, ensures good accuracy properties while maintaining enough generalization characteristics. The effectiveness of the proposed fault detection method is evaluated by means of tests on emulated and real chiller data. The KPCA approach is first proved to exhibit better detection performances than linear PCA, and then, it is corroborated through the comparison with local outlier factor, one-class support vector machine, and isolation forest.

Efficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption. The predictive control is compared with a rule-based technique, herein referred to as a heuristic approach, that acts in an instant-by-instant fashion without considering any future information. The reported results show that the studied predictive approach allows one to achieve charging profiles that adapt to variable operating conditions, aiming at optimal performances in terms of economic cost minimization in time-varying price scenarios, reduction of rms current stresses, and recharging capability of EV batteries. Specifically, unlike the heuristic method, the MPC approach is proven to be capable of efficiently managing the available energy resources to ensure a full recharge of the EV battery during nighttime while always respecting all system constraints. In addition, the proposed control is shown to be capable of keeping the peak power absorption from the grid constrained within set limits, which is a valuable feature in scenarios with widespread adoption of EVs in order to limit the stress on the electrical system.

The diffusion of distributed energy resources in distribution networks requires new approaches to exploit the users’ capabilities of providing ancillary services. Of particular interest will be the coordination of microgrids operating as an aggregate of demand and supply units. This work reports a model predictive control (MPC) application in microgrids for the efficient energy management of energy storage systems and photovoltaic units. The MPC minimizes the economic cost of aggregate prosumers into a prediction horizon by forecasting generation and absorption profiles. The MPC is compared in realistic conditions with a heuristic strategy that acts in a instant manner, without taking into account signals prediction. The work aims at investigating the effect that different types of energy tariffs have in enhancing the end-users’ flexibility, based on three examples of currently applied tariffs, comparing the two storage control modes. The MPC always achieves a better solution than the heuristic approach in all considered scenarios from the cost minimization point of view, with an improvement that is amplified by increasing the energy price variations between peak and off-peak periods. Furthermore, the MPC approach provides a cost saving when compared to the case considering a microgrid endowed with only photovoltaic units, in which no storage is installed. Findings in this work confirm that storage units better perform when some knowledge of future demand and supply trends is provided, ensuring an economic cost saving and an important service for the overall community.