Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy field, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Verification Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated.

The digital world is spreading to all sectors of the economy, and Industry 4.0, with the digital twin, is a reality in the building sector. Energy reduction and decarbonization in buildings are urgently required. Models are the base for prediction and preparedness for uncertainty. Building energy models have been a growing field for a long time. This paper proposes a novel calibration methodology for a building energy model based on two pillars: simplicity, because there is an important reduction in the number of parameters (four) to be adjusted, and cost-effectiveness, because the methodology minimizes the number of sensors provided to perform the process by 47.5%. The new methodology was validated empirically and comparatively based on a previous work carried out in Annex 58 of the International Energy Agency (IEA). The use of a tested and structured experiment adds value to the results obtained.

There is growing concern about how to mitigate climate change in which the reduction of CO₂ emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simplified past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model.