During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided.

The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations.

The building sector has an important role in the decarbonization of energy. Buildings energy management systems can act on flexible loads to contribute to the integration of renewable energies. This article presents the design, implementation and evaluation of a rule-based energy management systems (RB-EMS) in the frame of a collective self-consumption (CSC) system based on photovoltaic (PV) energy in the Technology Park of Izarbel, in Bidart, France. The RB-EMS acts on the heat ventilation and air conditioning (HVAC) system of one of the buildings involved in the CSC in order to maximize the PV energy self-consumption rate (SCR). Internet of things (IoT) has been developed and implemented in order to retrieve consumption, production and temperature data, and to be able to act on the HVAC. A new 5-step methodology has been developed to design and adjust the RB-EMS. This methodology is mainly based on tests carried out to find the relationship between ON/OFF states of the internal units of the HVAC and the heat pumps’ consumption. Finally, the RB-EMS is tested in heating operating mode. The results show that the RB-EMS allows obtaining a SCR of 89%, and that the users comfort limits are respected.