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Given the global energy and environmental situation, the European Union has been issuing directives with increasingly demanding requirements in term of the energy efficiency in buildings. The international competition of sustainable houses, Solar Decathlon Europe (SDE), is aligned with these European objectives. SDE houses are low energy solar buildings that must reach the near to zero energy houses’ goal. In the 2012 edition, in order to emphasize its significance, the Energy Efficiency Contest was added. SDE houses’ interior comfort, functioning and energy performance is monitored. The monitoring data can give an idea about the efficiency of the houses. However, a jury comprised by international experts is responsible for carrying out the houses energy efficiency evaluation. Passive strategies and houses services are analyzed. Additionally, the jury's assessment has been compared with the behavior of the houses during the monitoring period. Comparative studies make emphasis on the energy aspects, houses functioning and their interior comfort. Conclusions include thoughts related with the evaluation process, the results of the comparative studies and suggestions for the next competitions.

Abstract A change in the thermal environment of an urban area affects health, living conditions, and energy consumption. In urban planning, urban parks are one of the methods for improving the thermal environment and saving cooling energy. Urban park construction can mitigate temperature, but it also causes urban development by increasing local attractiveness. To achieve efficient energy saving through parks in urban planning, the purpose of this study is to investigate the relationship between building energy consumption and urban characteristics both before and after the construction of an urban park. This study targeted Seoul's Gyeongui line forest, which was recently converted into a linear park on the former railway as an urban regeneration project. We analyzed the relationship between energy consumption and urban characteristics using a regression model, focusing on the changes before and after the construction. In this study, urban characteristics include environment, building physical characteristics, and economic variables. The results show that the construction of the urban park reduced not only temperature but also building energy consumption. The energy reduction effect of the park was limited to a marginal distance. Meanwhile, the urban park construction caused land prices to rise and prompted new development, and this changed the urban characteristics of the area and affected energy consumption. Despite changes in urban characteristics, urban park planning is an effective methods of reducing the energy consumption involved in cooling urban areas. We recommend comprehensive consideration of the urban factors when making park policy to reduce urban temperature and energy consumption.

Abstract This paper empirically evaluates the extent of energy resilience achieved in a socially-deprived community in Oxford, through deployment of solar photovoltaic (PV) systems and smart batteries (internet enabled and controllable) across a cluster of 82 dwellings (households). The methodological approach comprised dwelling and household surveys, along with high frequency monitoring of household electricity consumption, solar PV generation, battery charge and discharge data. In the monitored households, average daily electricity consumption was found to be positively related with dwelling size, number of occupants and number of appliances used. Although 117 MWh of PV electricity was generated within a year across 74 dwellings, peak generation did not match peak consumption, demonstrating the need for battery storage. Home batteries were found to increase self-consumption of PV electricity and offset grid demand through discharge of stored PV electricity marginally at an average of 6%, depending on the size of the PV system, surplus PV electricity available and size of the battery. Aggregating solar generation and storage at a community level showed that peak grid electricity demand between 17:00 and 19:00 was reduced by 8% through the use of smart batteries across 74 dwellings. In future, a local energy sharing scheme could be developed, wherein not all dwellings would need to have solar PV systems, but rather have internet enabled batteries that could be monitored and controlled virtually.

Abstract A numerical simulation program, based upon a finite difference nodal network, was used to simulate two Los Alamos test cells (one single and one multi-room cell) and a typical residence — all of which were exposed to intense insolation and large changes in ambient weather conditions. For the test cells, the predicted surface and globe temperatures are in good agreement with the measured values and indicate the acceptability of thermal modeling. The program was used to predict the behavior of a residential structure. The process of refining these predictions, guided by observations, led to the development of a stepwise simulation methodology. The insights gained as a result of this interdisciplinary involvement have been stimulating and instructive. The importance of recognizing the differences in the thought processes and the work styles of the several professions has been demonstrated. The most effective simulation methodology was that based upon human comfort, which appeared to be a common perception amongst all the program users.

Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelli ...

Abstract Energy conservation measures (ECM) through awareness, good housekeeping measures and demand side management are the first step that must be implemented to achieve energy savings in any building. The energy savings from the implemented ECM must be properly quantified to demonstrate its effectiveness and success. Quantifying energy savings using Multiple Linear Regression (MLR) model have been gaining popularity due to its simple approach but its inability to model and predict dynamic behavior that involves non-linearity relationships can be seen as its main drawback. Thus, the objective of this work is twofold, where the first fold is to implement an ECM program in a building and the second fold is to determine the energy saved from the ECM program using Non-Linear autoregressive with Exogenous inputs artificial neural network (NARX-ANN). The ECM program was executed at one floor of a university building. NARX-ANN was used to model the baseline energy consumption and determine the energy savings following the International Performance Measurement and Verification Protocol (IPMVP). A comparison was made between the MLR and the NARX-ANN model where the latter provides higher accuracy in statistical error compared to the MLR model. The energy savings from the awareness program determined by the NARX-ANN model was 170.84 kWh with Mean Square Error and Root Mean Square accuracy of 0.6068 and 0.7790 respectively.

Passive buildings compared to the standard ones require significantly less energy for heating, so the correct models of every “energy using” building's components are very important. This paper analyzes how various models of the internal heat and moisture gains, as well as natural airflows between building zones, influence the accuracy of the calculation of the energy performance, indoor temperatures and absolute humidity in a single-family passive building. A simulation environment used a detailed twelve-zone TRNSYS model of a house with HVAC system. The model included natural airflows between zones, and internal heat and moisture gains, defined as precisely as possible. The gains were allocated on the basis of special protocols of use filled by the occupants during the two-week measurement. The measurement data were also used for validation of the model. The verified model constituted a basis for calculation of energy performance and simulation of air temperature and absolute humidity change in a building with significantly limited airflow between zones, and heat and moisture gains defined according to standards. The standardized values of heat and moisture gains were defined on the basis of the standard ISO 13790 and national regulations in Poland. The simulations have shown that precise methodology of calculation of heat gains and airflows between building zones is very important for proper computation of energy performance and simulation of indoor temperatures and absolute humidity in passive buildings. Results of carried out analysis have shown that the difference in energy need for heating calculated using precise and simplified methods of internal heat gains determination was 30.1%.

Abstract Traditional whole building energy modeling suffers from several factors, including the large number of inputs required for building characterization, simplifying assumptions, and the gap between the as-designed and as-built building. Prior work has attempted to mitigate these problems by using sensor-based machine learning approaches to statistically model energy consumption, applying the techniques primarily to commercial building data, which makes use of hourly consumption data. It is unclear, however, whether these techniques can translate to residential buildings, since the energy usage patterns may vary significantly. Until now, most residential modeling research only had access to monthly electrical consumption data. In this article, we report on the evaluation of seven different machine learning algorithms applied to a new residential data set that contains sensor measurements collected every 15 min, with the objective of determining which techniques are most successful for predicting next hour residential building consumption. We first validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, confirming existing conclusions that Neural Network-based methods perform best on commercial buildings. However, our additional results show that these methods perform poorly on residential data, and that Least Squares Support Vector Machines perform best – a technique not previously applied to this domain.