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Today, identifying energy poverty is one of the scientific community's challenges. In this sense, using Geographic Information Systems (GIS) assumes getting to know which areas are the most vulnerable. This study considers the development of a new territory energy poverty evaluation indicator, called Poverty Adaptive Degree Hourly Index (PADHI). The novelty of this indicator is that it combines the number of inhabitants in poverty with the hourly heating and cooling degrees of each climate, determined using adaptive thermal comfort limits based on ASHRAE 55-2017. The area studied focuses on Chile and its 346 municipalities. The application of the adaptive comfort model can exceed 90% in the north of the country and be below 20% in the most meridional areas. The results also outlined that the municipalities defined as PADHI's 10th decile are those which have a higher risk of energy poverty in their population, a higher percentage of income poverty and climatic severity. A cluster analysis was also run for this decile, identifying two risk groups with the aim of identifying the critical areas. This methodology applied to Chile, but international in nature, allows identifying priority areas on which the measures needed to reduce energy poverty can be focused. • Development of the Poverty-Adaptive Degree Hourly Index (PADHI) • Analysis of Chile's municipalities • It is detected that the country's southern municipalities have a lower energy poverty risk. • The most populated areas and those with higher energy demands are the most vulnerable municipalities. [ABSTRACT FROM AUTHOR]

• Fuel Poverty Potential Risk Index (FPPRI) applied to Santiago, Concepción, and Valparaiso (Chile). • A total of 116,640 cases were analysed, considering 9 morphological variables per decile. • Combination of 84 datasets using 2 approaches. • Performance greater than 96% of the individual models for each climate zone. Many studies are focused on the diagnosis of fuel poverty. However, its prediction before occupying households is a developing research area. This research studies the feasibility of implementing the Fuel Poverty Potential Risk Index (FPPRI) in different climate zones of Chile by means of regression models based on artificial neural networks (ANNs). A total of 116,640 representative case studies were carried out in the three cities with the largest population in Chile: Santiago, Concepción, and Valparaiso. Apart from energy price (EP) and income (IN), 9 variables related to the morphology of the building were considered in approach 1. Furthermore, approach 2 was developed by including comfort hours (NCH). A total of 84 datasets were combined considering both approaches and the 5 most unfavourable deciles according to the income level of Chilean families. The results of both approaches showed a better performance in the use of individual models for each climate (MLP C , MLP S , and MLP V), and the dataset with all deciles (Full) could be used. Regarding the influence of the input variables on the models, IN was the most determinant, and NCH becomes important in approach 2. The potential of using this methodology to allocate social housing would guarantee the main objective of the country: the reduction of fuel poverty in the roadmap for 2050. [ABSTRACT FROM AUTHOR]

Abstract Fuel poverty is a pertinent issue for vulnerable households both in industrialized and developing countries, which is related to energy prices and accessibility of energy services. This research explores the feasibility of predictive models to prevent fuel poverty through the Fuel Poverty Potential Risk Index (FPPRI). Two statistical models, multiple linear regression (MLR) and artificial neural networks (ANN), have been developed and applied to predict the probability of low-income households falling into fuel poverty when being allocated a social dwelling. The case study used to validate the model is located in the Bio-Bio Region of Chile and the households considered belong to the most vulnerable social strata. The models have considered the design and constructive features of common typologies of Chilean social dwellings, family income levels, changes in energy usage patterns and energy prices. Through extensive simulation and testing, ANNs have been found to be more accurate than MLRs for all situations, with a R2 coefficient above 99.6% and 80.7% respectively, despite their greater complexity. The result of this research can be useful in providing tools to fairly and accurately assign social dwellings to vulnerable households to prevent them from falling into fuel poverty. Highlights • Comparison of multiple linear regression and artificial neural networks models. • Multiple linear regression models with an R2 coefficient above 80.7%. • Multilayer perceptron accuracy with an R2 coefficient above 99.6%. • Fuel Poverty Potential Risk Index for social dwellings allocation. • Integration of adaptive comfort algorithms to assess the risk of fuel poverty. [ABSTRACT FROM AUTHOR]

An attempt has been made to develop linear regression models and Artificial Neural Networks (ANN) to predict the heating and cooling energy demands, energy consumptions and CO 2 emissions of office buildings in Chile. The calculation of dependent variables to calibrate and evaluate the models has been determined starting from the ISO 13790:2008 standard, assigning constructive characteristics to each of the geometries studied based on the Chilean standards, studying 77,000 cases. A total of 8 fundamental variables have been considered to cover the design parameters. In energy consumption and CO 2 emissions cases, the linear regression models that offer a better performance are those where the predictive variables have been transformed. Whereas, the multilayer perceptron adjusted over the variables without being transformed, provides greater accuracy in the determination of the demand, consumption and CO 2 emissions both for heating and cooling, offering ECM values closer to 0, with an R 2 coefficient above 99%. It is foreseen that the models developed can be used to estimate the energy saving between different design outlines during the project phases when the construction standards, systems and internal loads are defined. [ABSTRACT FROM AUTHOR]

Numerous studies about climate change have emerged in recent years because of their potential impact on many activities of human life, amongst which, the building sector is no exception. Changes in climate conditions have a direct influence on the external conditions for buildings and, thus, on their energy demand. In this context, computer aided simulation provides handy tools that help in assessing this impact. This paper investigates climate data for future scenarios and the effect on energy demand in office buildings in Chile. This data has been generated in the 9 climatic zones that are representative of the main inhabited areas, for the years 2020, 2050 and 2080. Predictions have been produced for the acknowledged A2 ‘medium-high’ Greenhouse Gases emissions GHG scenario, pursuant the Intergovernmental Panel on Climate Change (IPCC). The effect of climate change on the energy demand for office buildings is optimized by implementing the calculation procedure of ISO-13790:2008, based on iterations of its envelope and form. As a result, this research clarifies how future climate scenarios will affect the energy demand for different types of office buildings in Chile, and how their shape and enclosure can be optimized. [ABSTRACT FROM AUTHOR]

• Analysis of the effect of climate change on the adaptive comfort model in Japan. • The Taiheiyō Belt will not see remarkable changes. • Hokkaidō and Tōhoku might be clearly influenced under the different scenarios. • Natural ventilation had percentages of application thorough the year below 40 %. Adaptive comfort models have emerged as a sustainable way of providing comfort in connection with local climate. Additionally, climate change has posed an additional challenge. In previous studies, the authors clarified how climate change would affect the feasibility of the adaptive comfort model at a worldwide scale, but local considerations for some countries remained unsolved. This study presents the first comprehensive research on the applicability of the adaptive comfort model in Japan not only for current, but also for future scenarios considering the climate change. Remarkable differences across Japanese regions were found, especially between the Northern underpopulated regions of Hokkaidō and Tōhoku and the cities that belong to the Taiheiyō Belt. In general, the adaptive comfort model will find application both in current and future scenarios, but natural ventilation will not play an important role. Special attention should be drawn to the potentials saving of cooling degrees that can be achieved if adaptive setpoint temperatures become commonplace in the future in the Kantō region. These results pave the way for the consideration of the adaptive comfort model as a resilient strategy to adapt to the future changes in climate scenarios for the building industry. [ABSTRACT FROM AUTHOR]

Many studies are nowadays focused on the application of energy conservation measures (ECMs) to building sector because of the reductions of both greenhouse gases and energy consumption set by the European Union. Heating, Ventilating and Air Conditioning (HVAC) systems are the main causes of most energy consumption in the existing buildings. One of the methods to reduce energy consumption is the modification of setpoint temperatures adapted to external climate conditions but maintaining acceptable comfort levels. The adaptive comfort model of ASHRAE 55–2017 uses the prevailing mean outdoor temperature, which records temperatures of the previous days weighted by a constant α which depends on weather changes. This paper analyses the possibilities of optimizing building energy saving with setpoint temperatures according to the α-value and the number of previous days considered. A total of 390 simulations were conducted by considering three representative climate zones of Spain. The results showed that the α-values between 0.4 and 0.6 had the lowest energy consumption. The use of a low number of previous days also achieved greater reductions in heating energy consumption, whereas a larger number of days was applied to cooling energy consumption. This study makes progress in using adaptive setpoint temperatures optimally to reduce the energy consumption in the building sector. • Modification of setpoints in 390 simulations to optimize energy consumption. • The α-values between 0.4 and 0.6 had the lowest energy consumption. • The number of previous days varied to optimize heating and cooling consumption. • A saving between 25.73 and 44.89% of the total energy consumption by using adaptive setpoint temperatures. • Better performance of the α-value from ASHRAE 55–2017 than that from EN 15251:2007. [ABSTRACT FROM AUTHOR]

The building sector is among the main energy users in the world, and its consumption patterns are strongly affected by changes in climate conditions. The consumption prediction in future scenarios is one of the greatest challenges. The application of recent techniques, such as adaptive thermal comfort strategies, constitutes an opportunity to reduce energy consumption. This study aims at clarifying their worldwide application potential in two scenarios, current and 2050, linking it to predictions on world population distribution and on the development level of countries. By interpolating 15,897 meteorological database, worldwide maps were made to quantify the applicability of the model and to show that such applicability would largely benefit both developing countries and most world population. • Interpolation of 15,897 meteorological database for energy savings potential. • 34.18% of the Earth's ground surface can apply adaptive comfort models. • Worldwide application potential in current and 2050 scenarios. • The adaptive comfort model can potentially lead to energy savings in current and 2050 scenario. [ABSTRACT FROM AUTHOR]