There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study's strategy offers great simplicity and a large saving in computational time.

Nowadays, building energy models (BEMs) are widely used, particularly in the assessment of energy consumption in buildings to address the potential savings that can be generated. The realisation of a dynamic energy model based on high-fidelity physics (white-box models) requires a tuning process to fit the model to reality, due to many uncertainties involved. Currently some research trends try to reduce this performance gap by modulating different types of experimental parameters such as: capacitances or infiltration. The EnergyPlus simulation software, in its latest versions, has implemented an object: HybridModel: Zone that calculates the infiltration and internal mass of buildings using an inverse modelling approach that employs only the measured indoor temperature data to invert the heat balance equation for the zone under study. The main objective of this paper is to reduce the execution time and uncertainties in the development of quality energy models by generating a new calibration methodology that implements this approach. This uses, as a starting point, a research created by the authors of this study, which was empirically and comparatively validated against the energy models developed by the participants in Annex 58. It is also worth highlighting the empirical validation of the HybridModel: Zone object, since it was activated in all scenarios where its execution is possible: periods of seven days or more of free oscillation and periods in which the building is under load. The findings are promising. The data generated with the new methodology, if compared with those produced by the baseline model, improve their resemblance to the real ones by 22.9%. While those of its predecessor did it by 15.6%. For this study, the two dwellings foreseen in Annex 58 of the IEA ECB project have been modelled and their real monitoring data have been used.