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Recently, demand flexibility has been highlighted as a promising distributed resource from the customer side, especially from industrial customers like commercial buildings, capable of providing grid support services. However, the quantification of demand flexibility is a complex process that requires a methodology including the requirements of both the grid operators and the customers. This paper proposes a novel approach to quantify the available demand flexibility of individual buildings, while taking into account the underlying building energy physics. The proposed approach constructs on the operational flexibility concept from the power systems, and extends it to include a comfort domain, identifying different flexibility parameters with the aim of giving a better insight into the flexibility potential of commercial buildings. This method includes a development of building energy simulations to assess the effects of weather variations, construction types, and comfort constrains on demand flexibility. The proposed quantification method has been validated using 15 different office building models and two different climate zones, i.e., the Netherlands and Texas, US. The results presented in this paper suggest that buildings located in a hot climate could offer higher flexibility potential during shorter time ranges, while buildings in a cold climate could offer lower flexibility potential but during longer time ranges. Determining these differences could potentially facilitate the dispatch of flexible demand resources, to assess their real potential, and to schedule demand flexibility between stakeholders. [ABSTRACT FROM AUTHOR]

Climatic conditions strongly affect the energy performance of buildings, and due to the synergy between global climate change and the Urban Heat Island (UHI) effect, the climatic conditions inside and outside the city are highly different. However, weather data collected at airports are commonly used for building energy simulations and these data do not take into account real temperature distribution in cities. Many studies in the literature address the topic but mostly considering only a couple of urban and non-urban stations. In this paper, the urban climate in Rome, Italy, is analysed after monitoring of the air temperature and relative humidity in four selected neighbourhoods and one reference station from October 2014 until September 2017. Rome is characterised by a composite urban pattern and high variability of building types, of which the four selected areas are representative. The heating degree days decrease up to 18% and cooling degree days increase up to 157% in the urban area with respect to the rural reference and the area most affected by the UHI is the city centre. The UHI is more intense in summer than in winter (average increase between 0.7 °C and 1 °C); while the diurnal and nocturnal UHI depends on the season and the neighbourhood. Then, the energy performance of a representative apartment block and a typical office building was computed using the measured data. Regarding the predicted energy performance, comparing the four urban sites and the reference site, the UHI causes a reduction of heating consumption up to 21% in residential building and 18% in the office building. An increase of cooling consumption up to 74% is instead computed for the residential building and up to 53% for the office building. [ABSTRACT FROM AUTHOR]

Climatic zoning for building energy efficiency applications is an important element in building energy policy and regulations. There are several methodologies available to conduct climatic zoning, providing significantly different results. Currently, there are no procedures to assess the validity of a proposed climatic zoning, hindering the decision to use one particular climatic zoning methodology instead of another. This paper introduces a quality index and a procedure to support the validation of climatic zoning. The procedure is based on building performance simulation results concerning the building stock that is targeted in the climatic zoning policy or program. Simulation results are used to calculate a new index, the Mean Percentage of Misclassified Areas (MPMA), which assesses the quality of the zoning under analysis. The capabilities of this procedure were demonstrated by the evaluation of four alternatives for the climatic zoning of Nicaragua, obtained using different methodologies and previously reported in the literature. The building stock used in this case study is composed of a few archetypes based on typical naturally ventilated dwellings in this country. Simulations were conducted using the program EnergyPlus for a total of 328 locations in Nicaragua. Degree-hours of discomfort based on the adaptive model of ASHRAE Standard 55 were used as a performance indicator. Results indicate that zoning obtained using cluster analysis and cooling degree-days may misclassify 1 out of 5 areas in Nicaragua (MPMA around 18–20%). This study concludes that the validation procedure and proposed index are useful for highlighting qualities and deficiencies of existing climatic zoning methods, particularly when these methods are used in less conventional applications, such as for policy making targeting naturally ventilated dwellings in tropical climates. The application of this procedure in more than 50 countries which adopt climatic zoning is foreseen as the next step in his area, substantially affecting the prescription of building materials and components worldwide. [ABSTRACT FROM AUTHOR]

Building performance simulations (BPS) are used to test different designs and systems with the intention of reducing building costs and energy demand while ensuring a comfortable indoor climate. Unfortunately, software for BPS is computationally intensive. This makes it impractical to run thousands of simulations for sensitivity analysis and optimization. Worse yet, millions of simulations may be necessary for a thorough exploration of the high-dimensional design space formed by the many design parameters. This computational issue may be overcome by the creation of fast metamodels. In this paper, we aim to find suitable metamodeling techniques for diverse outputs from BPS. We consider five indicators of building performance and eight test problems for the comparison six popular metamodeling techniques – linear regression with ordinary least squares (OLS), random forest (RF), support vector regression (SVR), multivariate adaptive regression splines, Gaussian process regression (GPR), and neural network (NN). The methods are compared with respect to accuracy, efficiency, ease-of-use, robustness, and interpretability. To conduct a fair and in-depth comparison, a methodological approach is pursued using exhaustive grid searches for model selection assisted by sensitivity analysis. The comparison shows that GPR produces the most accurate metamodels, followed by NN and MARS. GPR is robust and easy to implement but becomes inefficient for large training sets compared to NN and MARS. A coefficient of determination, R 2 , larger than 0.9 have been obtained for the BPS outputs using between 128 and 1024 training points. In contrast, accurate metamodels with R 2 values larger than 0.99 can be achieved for all eight test problems using only 32–256 training points. [ABSTRACT FROM AUTHOR]