► A review on quantitative energy assessment methods and studies are presented. ► Methods are categorized into calculation-based, measurement-based and hybrid methods. ► Assessment methods can be ...used for performance classification and diagnosis. ► Existing methods assess energy performance at building level or/and multiple levels.
Building energy performance assessment is crucial to ascertain the efficiency of energy use in buildings and is the basis to make any decision for enhancing energy efficiency. In order to assess the energy performance of existing buildings quantitatively, the energy use of the assessed buildings should be quantified first. The quantified energy use will be then used to compare with the assessment criteria to determine the energy performance quantitatively. This paper presents an overall review on the state of the art of the research and applications of quantitative energy performance assessment. A framework is proposed for categorizing the energy quantification methods and performance benchmarking methods for energy performance assessment for existing buildings. Energy quantification methods are classified into three categories, i.e. the calculation-based, measurement-based and hybrid methods, according to the energy data acquisition approaches. Energy performance assessment methods are classified according to the assessment scope and depth of assessment, i.e. whole-building benchmarking method at building level and multi-level assessment method.
•A compound storage system combines seasonal cold storage with chilled water storage.•It enables smaller scale seasonal cold storage to be used in small size buildings.•Design optimization for ...seasonal ice storage and the compound system are presented.•Natural cold energy is charged in winter and used for free cooling in summer.•Life-cycle cost of building cooling system can be reduced by 40%.
Seasonal cold storage using natural cold sources for cooling is a sustainable cooling technique. However, this technique suffers from limitations such as large storage space and poor reliability. Combining seasonal storage with short-term storage might be a promising solution while it is not explored sufficiently. This paper presents a compound cold storage system that combines a heat pipe-based seasonal ice storage system with a chilled water storage system. The seasonal ice storage system automatically charges winter cold energy in the form of ice. In summer, the stored ice is extracted for cooling, and then the melting ice is used as a chilling medium for chilled water storage. Design optimization of the seasonal ice storage system and the compound storage system is addressed, including the sizes of heat pipes, the configuration and volume of the cold storage tank and the chiller capacity. A case study is conducted to demonstrate the design and the application of the proposed system in a real building in Beijing. Results show that the appropriate combination of the two types of cold storage can greatly improve the applicability of the seasonal cold storage and reduce the life-cycle cost of a building cooling system by 40%.
Seasonal cold storage is a high-efficient and environmental-friendly technique that uses the stored natural cold energy in winter (e.g., snow, ice or cold ambient air) for free-cooling in summer. ...This paper presents a seasonal cold storage system that uses separate type heat pipes to charge the cold energy from ambient air in winter automatically, without consuming any energy. The charged cold energy is stored in the form of ice in an insulated tank and is extracted as chilled water for cooling supply in summer, which help to reduce the chiller running time and reduce the associated electricity consumption and greenhouse gas emission significantly. A quasi-steady two-dimensional mathematical model of the system is developed for characterizing the dynamic performance of ice growth (i.e., cold charging). The model is validated using the field measurement data from an ice charging experiment conducted in Beijing. The impacts of various affecting factors, including the weather data and the key parameters of heat pipes, on the charging performance of the cold storage system are analyzed. The effectiveness and sustainability of the proposed system for cooling are demonstrated through a case study of a kindergarten building in Beijing.
•A seasonal cold storage system is presented for sustainable building cooling.•Cold energy can be charged by separate type heat pipes without using any energy.•The stored nature cold energy is used for free cooling in summer.•A quasi-steady two-dimensional model is developed for cold charging.
The classroom environment is of great significance for the health of primary and secondary school students, but a comfortable indoor environment often requires higher energy consumption. This paper ...presents a multi-objective optimization method based on an artificial neural network (ANN) model, which can help designers efficiently optimize the design of primary and secondary school classrooms in southern China. In this optimization method, first, the optimization objectives and variables are determined according to building characteristics, and the physical model is established through simulation software (EnergyPlus) to generate the sample space. Second, sensitivity analysis is carried out for each optimization variable, and the physical model is modified according to the results to regenerate the sample space. Third, the ANN model is trained by using the regenerated sample space, and the Pareto optimal solution is generated through the use of the non-dominated sorting genetic algorithm II (NSGA-II). Finally, the effectiveness of the multi-objective optimization method is proven through a typical case of primary and secondary school classrooms in Nanjing, China. The results show that, compared with the benchmark scheme, TES decreased by 810.8 kWh at most, PT increased by 47.8% at most and DI increased by 4.2% at most.
Building heating, ventilation, and air conditioning (HVAC) systems consume large amounts of energy, and precise energy prediction is necessary for developing various energy-efficiency strategies. ...Energy prediction using data-driven models has received increasing attention in recent years. Typically, two types of driven models are used for building energy prediction: sequential and parallel predictive models. The latter uses the historical energy of the target building as training data to predict future energy consumption. However, for newly built buildings or buildings without historical data records, the energy can be estimated using the parallel model, which employs the energy data of similar buildings as training data. The second predictive model is seldom studied because the model input feature is difficult to identify and collect. Herein, we propose a novel key-variable-based parallel HVAC energy predictive model. This model has informative input features (including meteorological data, occupancy activity, and key variables representing building and system characteristics) and a simple architecture. A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model. A case study is conducted to screen the key variables of hotel buildings in eastern China, based on which a parallel chiller energy predictive model is trained and tested. The average cross-test error measured in terms of the coefficient of variation of the root mean square error (CV-RMSE) and normalized mean bias error (NMBE) of the parallel chiller energy predictive model is approximately 16% and 8.3%, which is acceptable for energy prediction without using historical energy data of the target building.
► A simplified multi-level energy performance assessment method for existing buildings. ► The method is based on electricity balance and cooling energy balance principles. ► An optimization algorithm ...is used to minimize cooling balance residuals. ► It disaggregates the electricity consumption of end-users based on electricity bills. ► It requires very limited data while providing results with acceptable accuracy.
This paper presents a simplified energy performance assessment method for existing buildings in cooling season, which is based on energy bill disaggregation and energy performance analysis. This method requests very limited building energy data and can effectively assess the energy performance at building and system levels, and disaggregate the whole-building consumption into consumptions of three groups of end-uses. This method is based on two basic energy balance principles, i.e., the electricity consumption balances at building level and the cooling energy balances between demand side and supply side of HVAC systems. An optimization algorithm is developed to establish best possible cooling energy balances by minimizing the balance residuals and therefore to disaggregate the energy consumption of different users. The performance of the proposed method was validated in two existing buildings in Hong Kong and Beijing. The results, including the disaggregated energy consumptions and the key energy performance indicators of HVAC system, agreed well with the “measured” data in mechanical cooling months.
This paper presents a new strategy using a limited number of sensors for demand-controlled ventilation (DCV) of multi-zone office buildings. The conventional CO2-based demand-controlled ventilation ...strategy for multi-zone offices requires CO2 sensors and supply airflow meters being installed in all zones. However, in many practical cases, CO2 sensors might not be available or not necessary. To control the outdoor airflow based on actual occupancy variations in such cases, a DCV strategy with two implementing schemes is developed for different sensor availabilities. The first scheme is used for the situation when CO2 sensors in individual zones are not available but the airflow meters for individual zones are installed. The second scheme is used for the conditions when CO2 sensors and airflow meters in individual zones are not available. These two schemes use two different approaches to estimate the outdoor airflow fraction of the critical zone approximately. Both schemes only require the CO2 sensor in the main return air to dynamically detect the total occupancy number. The developed strategy is implemented and validated in a high-rise office building in Hong Kong. The site test results show that the strategy can achieve significant energy saving while maintaining acceptable IAQ in the situations where only limited sensors are available.
► A new strategy using limited sensors for demand-controlled ventilation of multi-zone offices. ► In-situ implementation and validation in a high-rise office building. ► The DCV strategy achieved significant energy saving while maintaining acceptable IAQ. ► The strategy has similar performance as a multi-zone DCV strategy with full sensors.
Purpose of Review
This paper reviews the data mining (DM)-related research and applications at the building operation stage. It aims to summarize DM-based solutions for building energy management and ...reveal current research and development outcomes in analyzing massive building operational data using advanced DM techniques.
Recent Findings
Previous studies mainly adopt DM techniques for two tasks, i.e., (1) predictive modeling; (2) fault detection and diagnosis. The knowledge discovered has been successfully utilized to facilitate the decision-making during building operations. Domain expertise play the dominant role in the knowledge discovery process, which limits the chance of discovering novel knowledge.
Summary
DM is a promising technology for the development of intelligent and automated building management systems. Despite encouraging results, more research efforts should be made in (1) exploring the usefulness of unsupervised DM, (2) developing generic analytic frameworks, and (3) analyzing unstructured and multi-relational data sets.
As the major electricity consumers worldwide, buildings can play an important role for power balance of smart grid through demand response (DR). Demand side-based control and supply side-based ...control are two typical types of DR measures when using centralized building air-conditioning systems for DR. For demand side-based control, the major disadvantage is that the response speed is generally too slow to allow buildings providing an immediate power reduction for the smart grid. For supply side-based control, the response speed is fast enough while it may cause control disorder to the whole system and uneven indoor temperature increase among different zones. In order to overcome above disadvantages, we proposed a novel DR method for building air-conditioning systems, which combines both the demand side-based and supply side-based control simultaneously. It consists of two major steps. First, some running chillers will be shut down to provide an immediate power reduction once urgent power reduction requests from smart grids are received by buildings. Second, the indoor air temperature set-points will be adjusted stepwise based on an "incremental schedule" to achieve a uniformly indoor temperature rise among all concerned zones/rooms. By implementing such two steps, an immediate power reduction is achieved while minimizing the uneven sacrifice of thermal comfort among different occupants. Two new performance indexes are proposed to evaluate the thermal comfort performance of DR methods. The proposed DR method is implemented and tested as case study in a virtual building dynamically simulated by TRNSYS. Five scenarios with different incremental steps for adjusting the temperature set-points are compared to determine the optimum "incremental schedule". Results show that buildings can provide immediate power reduction and achieve a small and even thermal comfort sacrifice by implementing the proposed compound DR method.
Conventional and most optimal design methods for chiller plants often address the annual cooling load distribution of buildings and their peak cooling loads based on typical meteorological year (TMY) ...data, while the peak cooling load only appears a few times during the life-cycle and the sized chiller plant usually operates within its low efficient region. In this paper, a robust optimal design method based on life-cycle total cost was employed to optimize the design of a chiller plant with quantified analysis of uncertainty and reliability. By using the proposed design method, the optimized chiller plant can operate at its highly efficient region under various cooling load conditions, and provide sufficient cooling capacity even alongside some equipment/systems with failures. The minimum life-cycle total cost, which consists of the capital cost, operation, and availability-risk cost, can be achieved through optimizing the total cooling capacity and the numbers/sizes of chillers. A case study was conducted to illustrate the detailed implementation process of the proposed method. The performance of this design method was evaluated by comparing with that of other design methods.
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