Buildings are one of the key factors in working towards a low-carbon economy to help mitigate climate change. For this reason, many of the current regulations aim to reduce their consumption and increase their efficiency, as is the case in the European Union with the Energy Performance of Buildings Directive (EPBD). Terms such as nearly zero-energy buildings (nZEB) or zero-emission buildings (ZEB) are increasingly used. However, these terms and regulations focus on energy and emissions, ignoring user comfort. This research shows the performance of these buildings in the face of climate change, as their strengths are not limited to energy consumption or emissions, but also to improving user comfort. By examining the compliance of a real semi-detached house with the different Spanish energy regulations (NBE-CTE 79, CTE-DB HE 2013 and CTE-DB HE 2019), its performance in terms of energy and comfort in different future scenarios defined by the Intergovernmental Panel on Climate Change (IPCC) is evaluated. The results show that the building with nZEB criteria (CTE-DB-HE 2019) reduces its energy consumption by an average of 84.36% compared to the other two energy standards. In terms of comfort, measured according to the Fanger criteria (steady state model), the hours throughout the year in the "neutral" thermal sensation category are similar; however, the hours in the "slightly cool" category are reduced by 57%, improving by up to eight times the "slightly warm" category. The nZEB building proves to be more resilient to climate change by mitigating and homogenizing its response to climatic variations.

The calibration of building energy models is crucial for their use in some applications that depend on their accuracy for adequate performance, such as demand response and model predictive control (MPC). In general, energy models offer many possibilities/strategies when characterizing a construction system, and such a characterization is key when analyzing both its thermal behavior and its energy impact. This research analyzes the different ways to characterize the thermal interaction of the building energy model (BEM) with the ground, comparing conventional approaches with new approaches based on both optimization of the former and dynamic ground characterizations. Using a model adjusted to a real case study, each of the existing options are analyzed, in which a different control of the ground temperature both in terms of its temporal oscillation and its location in the building (based on thermal zones) is taken into account. Exhaustive monitoring of a real building and measuring the ground and ground floor surface temperatures have made establishing which EnergyPlus components/objects best characterize the ground-slab interaction possible, both in terms of the simplicity of modeling and the cost (economic and technical) required for each of them. As will be seen, there are objects with an excellent cost/effectiveness ratio when characterizing the ground. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In the fight against climate change, energy modeling is a key tool used to analyze the performance of proposed energy conservation measures for buildings. Studies on the integration of photovoltaic energy in buildings must use calibrated building energy models, as only with them is the demand curve real, and the savings obtained at the self-consumption level, energy storage in the building, or feed into the grid are accurate. The adjustment process of a calibrated model depends on aspects inherent to the building properties (envelope parameters, internal loads, use schedules) as well as external to them (weather, ground properties, etc.). Naturally, the uncertainty of each is essential to obtaining good results. As for the meteorological data, it is preferable to use data from a weather station located in the building or its surroundings, although this is not always possible due to the cost of the initial investment and its maintenance. As a result, weather stations with public access to their data, such as those located at airports or specific locations in cities, are largely used to perform calibrations of building energy models, making it challenging to converge the simulated model with measured data. This research sheds light on how this obstacle can be overcome by using weather data provided by a third-party company, bridging the gap between reality and energy models. For this purpose, calibrations of the two buildings proposed in Annex 58 were performed with different weather configurations, using the mean absolute error (MAE) uncertainty index and Spearman's rank correlation coefficient (rho) as comparative measures. An optimal and cost-effective solution was found as an alternative to an on-site weather station, based on the use of a single outdoor temperature sensor in combination with third-party weather data, achieving a robust and reliable building energy model.

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R-2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building¿s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside¿thermal sensors¿and outside¿weather station¿is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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 a growing concern about how to mitigate climate change, in which the production and use of energy has a great impact as one of the largest sources of global greenhouse gases (GHG). Buildings are responsible for a large percentage of these emissions. Therefore, there has been an increase in research in this area, in order to reduce their consumption and increase their efficiency. One of the major simulation programs used in optimization research is EnergyPlus. The purpose of this software is the complete energy simulation of a building, although it lacks tools to analyze its results and, above all, to manage and edit its simulations. For this reason, we developed an application programming interface (API) that serves to merge two areas which are highly demanded by researchers: energy building simulation (using EnergyPlus) and tools for the management and design of research experiments (in this case, MATLAB®). The developed API allows the user to perform complex simulations using EnergyPlus in a simple way, as it allows the editing of each simulation and the analysis of the simulation results through MATLAB®. In addition, it enables the user to simultaneously run multiple simulations, using either all computer core processors or a selection of them (i.e., allowing parallel computing), reducing the simulation time. The API was developed in the C# language, such that it can be used with any software that can import .NET libraries.

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoo

The self-consumption without surplus to the grid is one of the aspects of the new Spanish law for prosumers. Increasing the share of renewable energy sources into the grid inherently leads to several constraints. The mismatch between the energy demand and the renewable energy production, which is intermittent in nature, is one of those challenges. Storage offers the possibility to decouple demand and supply, and therefore, it adds flexibility to the electric system. This research evaluates expanding electricity self-consumption without surplus to the grid by harnessing thermal mass storage in the residential sector. The methodology is investigated by using a variable refrigerant flow air conditioner system. Because there is no option to export the excess capacity to the grid, this research proposes an approach to profiting from this surplus energy by activating structural thermal mass, which is quantified from the information acquired using a building energy model. For this purpose, an EnergyPlus model of a flat in Pamplona (Spain) was used. The optimization analysis was based on a set-point modulation control strategy. Results show that under adequate climatological circumstances, the proposed methodology can reduce the total electric energy from the grid between by 60¿80%.

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building¿s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along

In the design of new buildings, project energy efficiency is nowadays one of the most important parameters on its development. The energy crisis on which we live and the greater social awareness have allowed efficiency be a major factor when we are thinking in a new building solution. This search for efficiency must be supported by studies to quantify these improvements. With this information we can assess which one of the different solutions is the best. The building sustainability is closely related to building efficiency. The present analysis shows through a semi-attached house, the importance of building orientation in heating and cooling demands. With this knowledge we can choose the orientation with low energy consumption.

"In the Department of Construction Science, we have developed and documented a specific model of systematized process in order to carry out activities of R&D&I oriented to a detailed design of constructive systems of innovative façades. This specific model will be used as a guide for the study, analysis, design and evaluation of the proposed solutions for façades. This model of systematized process of research provides a general idea of the set of stages and activities in the processes of R&D&I, with a graphical map and a documentary structure that describes how all the process works, step by step, facilitating its comprehension and application. The protocol details the key procedures within the global process. This protocol allows elaborating the programme of necessities derived from the conditionings, requirements and demands related to every specific façade, assisting with the definition of the technical characteristics of its parts and components to approach the conceptual and graphical design of constructive solutions, in successive stages. This protocol provides the means to analyze and evaluate every particular design of façade and the process of R&D&I that has been followed, so that it allows to justify and demonstrate the fulfilment of the requirements and demands that concern every stage of the complete life cycle of façades, and to identify fields that could be improved in the R&D&I process, in order to apply corrective actions. This protocol is complemented by som