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Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants. 33 pages, 18 figures

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.

Abstract Increased building indoor air quality (IAQ) complaints due to reduced outdoor air ventilation rates led to ASHRAE Standard 62–1989. Even though the stipulated standard total outdoor ventilation flow rate may be drawn into the HVAC system, thermal imbalances in the various zones of the building can lead to certain zones being starved of the specified ventilation flow rate, thereby creating localized IAQ problems. The objective of this paper is to compare the differences in energy use and ventilation air flow rates supplied to different zones in the building for three different practical outdoor air ventilation strategies all of which are identical in performance at design conditions but which differ under part-load operation. A simplified simulation methodology (which past studies have demonstrated to be useful for field evaluation of actual buildings) has been used to predict the heating and cooling energy use of a two-zone terminal reheat variable air volume (TRVAV) system during part-load operation specified by varying outdoor temperature and humidity conditions. The trade-off between outdoor air intake and energy use are studied for the following ventilation strategies for a typical 10,000 m 2 commercial building: (i) constant outside air intake based on a value 20% higher than the ASHRAE minimum ventilation rate, (ii) constant ventilation air intake fraction, and (iii) ventilation air intake based on the unfavorable zone requirements (even though the other zone may be over-ventilated). How this trade-off is impacted by bullding size has also been investigated. Finally, we use bin data for Dallas, TX (a moderately hot and humid location) and Seattle, WA (a mild location) in order to study the differences in energy use and zone ventilation flows of different ventilation strategies due to building location. The effect of economizer cycles and of varying ventilation strategies depending upon diurnal building schedules have not been considered in this study. The results of this study which are based on a simplified HVAC simulation approach are consistent with conclusions reached by other researchers Using more detailed simulation models. This suggests that sound and meaningful diagnostic insights of actual building performance and operating strategies can be obtained from such simplified simulations.

Abstract As the size of buildings and demands on large centralized heating and cooling systems increases concurrent with rapid worldwide urbanization, the energy impact of hydronic distribution systems will become increasingly important in reducing greenhouse gas emissions. Further, in the U.S., the growth in multi-family buildings and the share of residential units in large multifamily buildings is far outpacing single-family construction. This paper describes a study of the pumping energy requirements of an urban 23-story mixed-use, primarily multifamily residential building before and after a suite of energy conservation measures. The retrofit focused on waterside technologies: Variable frequency drives (VFDs), constant and variable speed pumps, and pressure-independent control valves. In the original building, the central pumping equipment was found to be responsible for 55% of total annual owner-metered electricity usage and 29% of all annual owner-paid utility bills. Using extensive in-situ monitoring and analytical models developed for this effort, the full retrofit was computed to achieve a 41% reduction in annual central pumping electricity, representing an annual savings of 12% of all owner-paid energy bills. The most significant energy impact is attributable to the VFDs, and it can be inferred that additional savings could be achieved by installing VFDs on constant speed pumps.

Abstract With the rise of awareness of health and well-being in cities, urban environmental analysis should expand from energy performance to new environmental quality-based considerations. The limited potential to annually evaluate outdoor thermal comfort, predominant among these considerations, has restricted the exploration of the interrelations between urban morphology and annual energy performance. This study aims to bridge this gap by capitalizing on the new capabilities of Eddy3D – a Grasshopper plugin which enables effective calculations of hourly microclimatic wind factors via OpenFOAM which in turn are used to generate annual outdoor thermal comfort plots. Using this method, a parametric study was conducted for different typology and density scenarios in three different hot climatic contexts in Israel. The automated analytical workflow evaluated a total of 60 design iterations for their energy balance, outdoor thermal comfort autonomy (OTCA) and self-shading levels using the shade index. The high correlation found here between the annual shade index and the OTCA, across all climatic contexts, shows the potential of the shade index to serve as an effective indicator, in these contexts, for comparative or optimization outdoor comfort studies. Further results are both the superiority of the courtyard typology in both energy and outdoor comfort studies, and the contrasting impact of higher density on the annual energy balance (lower performance) and outdoor thermal comfort (higher performance) in hot climates. The annual plots of both the energy balance and OTCA reveal various seasonal and monthly trends in the three different climatic zones which can lead to localized and seasonal urban design strategies.

Ventilated windows have shown great potential in conserving energy in buildings and provide fresh air to improve indoor air quality. This paper reports our effort to use EnergyPlus to simulate the energy performance of a dual airflow window under different climates. Our investigation first developed a network model to account for the two-dimensional heat transfer in the window system and implemented it in EnergyPlus. The two-dimensional assumption and the modified EnergyPlus program were validated by the measured temperatures of the window and the energy demand of a test cell with the window under actual weather conditions. Then EnergyPlus was applied to analyze energy performance of a small apartment installed with the dual airflow windows in five different climate zones in China. The energy used by the apartment with blinds windows and low-e windows was also calculated for comparison. The dual airflow window can reduce heating energy of the apartment, especially in cold climate. The cooling energy reduction by the window was less important than that by shading solar radiation. The dual airflow window is recommended for colder climate. If improving air quality is a major consideration for a building, the window can be used in any climate.

Abstract A Solar Chimney (SC) is a passive system harnessing solar energy to ventilate inner spaces and improve occupants’ thermal comfort. The purpose of this study is to determine the time periods of a year most suitable for the use of a SC in terms of thermal comfort under the different climates of Bandar-Abbas (hot and humid), Yazd (hot and arid), Paris (mild and humid) and Toronto (cold and humid). To this aim, a thermosyphonic model – validated against published experimental data – was employed for running dynamic thermal simulations of a residential building using the EnergyPlus software. The optimal time periods of operation of the SC, driven by the values of the Predicted Mean Vote (PMV) between −0.7 and +0.7 and Predicted Percentage of Dissatisfied (PPD) below 15%, are found to be November and January to April in Bandar-Abbas (2265 h), while for the city of Yazd it comprises the months of April and May together with September and October (2145 h). As for Paris and Toronto, the period spans from June to August, for a total of 1065 and 1620 h respectively. Correlation coefficients disclosing the impact of the increased air mass flow rate revealed a positive impact everywhere during nighttime in terms of indoor air temperatures (from 0.02 in Bandar-Abbas to 0.77 in Yazd). On the other hand, the impact on indoor relative humidity is always negatively correlated, with a peak of −0.73 in Yazd. Year-long comfort conditions are finally displayed in psychometric charts against ASHRAE 55 Standard boundaries.

Abstract Ventilation is essential for the maintenance of good indoor air quality, although there is evidence to suggest that energy loss through uncontrolled or unnecessary air infiltration is excessive. In this study, estimates are presented for air change (ventilation and infiltration) energy use in non-industrial buildings for 13 countries. Various methods are used for the estimates, but they are mainly based on calculating the total annual enthalpy change needed for the conditioning of air. The potential for reduced energy use by improved ventilation control is also briefly reviewed. Considering the non-industrial building stock of the 13 countries collectively, the total annual loss of heating energy due to air change is estimated to amount to 48% of delivered space conditioning energy (including heating equipment losses). The results emphasise that air change related energy losses are as important as conduction and equipment losses (including ‘flue’ losses) in dissipating delivered space conditioning energy from buildings. In addition, estimated financial expenditures, as well as carbon dioxide emissions associated with this energy use are indicated. The financial expenditures are derived from the air change energy estimates using published International Energy Agency (IEA) energy pricing information. The magnitudes of the carbon dioxide emissions have been based on published IEA estimates of total emissions from energy-related sources for each country.

The present research work concerns development of regression models to predict the monthly heating demand for single-family residential sector in temperate climates, with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions. Another interest to use such simplified models is to make it possible a very quick parametric study in order to optimize the building structure versus environmental or economic criteria. All the energy prediction models were based on an extended database obtained by dynamic simulations for 16 major cities of France. The inputs for the regression models are the building shape factor, the building envelope U-value, the window to floor area ratio, the building time constant and the climate which is defined as function of the sol-air temperature and heating set-point. If the neural network (NN) methods could give precise representations in predicting energy use, with the advantage that they are capable of adjusting themselves to unexpected pattern changes in the incoming data, the multiple regression analysis was also found to be an efficient method, nevertheless with the requirement that an extended database should be used for the regression. The validation is probably the most important level when trying to find prediction models, so 270 different scenarios are analysed in this research work for different inputs of the models. It has been established that the energy equations obtained can do predictions quite well, a maximum deviation between the predicted and the simulated is noticed to be 5.1% for Nice climate, with an average error of 2%. In this paper, we also show that is possible to predict the building heating demand even for more complex scenarios, when the construction is adjacent to non-heated spaces, basements or roof attics.