Building retrofitting is an efficient means of reducing greenhouse gas emissions. Its first focus is on building facade, as transmission and air leakage are the main sources of energy loss in buildings. Nowadays, building modellers cannot easily implement envelope air leakage and assume constant values, which results in erroneous energy estimates. Additionally, in energy simulations, a weather file is usually inserted with measurements provided by a weather station. In this study, we revealed the use of wind data from the weather file (herein as global wind) to calculate the infiltration of a test case in Spain, using the three algebraic equations of EnergyPlus. Furthermore, four other wind data were applied: eastbound and westbound winds from the weather file and two from in situ measurements (on the southeast and on the northwest facades). The fifteen combinations of the three infiltration models and the five wind data were empirically evaluated, using the tracer gas results performed during three different periods. The combinations were validated according to the American Society for Testing Materials D5157 standard criteria, and the best and the only ones that complied with the standard were those using the wind data from the southeast in situ sensor and the west wind from the weather station. The global wind was not able to generate accurate infiltration models, which raises doubts about its use in the highly-time calibration of energy models. However, its disaggregation was a cost-effective strategy to estimate the infiltration of this case study.

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.

The European Council has proposed reducing buildings' energy consumption as one way to decarbonization by 2050. Currently, digital twins are used for real-time energy management, but there are discrepancies between predicted and measured energy performance due to uncertainties in building energy models (BEMs). Air leakage is a key parameter that is difficult to obtain and EnergyPlus users often employ a constant value or apply air leakage equations with pre-determined coefficients. This research is a preliminary step in reducing this uncertainty in the author's methodology of BEMs calibration, using EnergyPlus and measured data. The study empirically verifies the ability of the EnergyPlus model's design flow rate to accurately replicate dynamic infiltration values within a zone of a high-rise residential building, where a tracer gas test using CO2 and a blower door test were conducted. Three new methods for calculating Idesrgn were developed and evaluated. The results were assessed based on the American Society for Testing Material D5157 (Standard Guide for Statistical Evaluation of Indoor Air Quality Models). The models generated with ad-hoc coefficients were compared to those from the literature (EnergyPlus, DOE-2, and BLAST). Among the models with off-the-shelf coefficients, the one with Ide,,gn calculated with in situ data and DOE-2 coefficients demonstrates an accuracy that is only 26% lower than the best model with regression coefficients, which has an R2 value of 0.94 and an NMSE value of 0.02 in the training period.