Cooling Load Demand Research Articles

Energy Management Strategy for Zone Cooling Load Demand Reduction in Commercial Buildings: A Data-Driven Approach

Optimal scheduling and robust sequence control of a central chiller system with thermal energy storage may not be a straightforward solution to reduce building cooling load demand (CLD) due to unpredictable thermal comfort requirements and the stochastic nature of occupancy-dependent CLD. One potential way to address this challenge is to design and develop an energy management strategy (EMS) for a zone-variable air volume unit. This paper proposes an EMS to reduce CLD by deciding the zone reference setpoint temperature based on fuzzy logic strategy (FLS) and zone thermal models. The objective is to reduce zone CLD while maintaining zone temperature and zone thermal comfort margin within the threshold limits. In this context, first, we modeled the zone thermal behavior using an artificial neural network (ANN) approach to predict zone temperature for next time slot, modeled the zone cooling energy behavior using a regression approach to predict zone CLD for the next time slot, and estimated the zone occupancy status using noise data. Second, we implemented zone thermal feedback model and comfort margin based on zone thermal conditions and predicted mean vote index, respectively. Finally, we implemented an EMS that includes both demand supply strategy and cooling load demand reduction strategy based on one time-slot ahead prediction. The novelty of the proposed EMS lies within the zone CLD reduction using FLS, predicting zone thermal behaviors using an ANN and a regression approach, estimating zone occupancy using noise data, and maintaining zone thermal comfort margin within the threshold limits. Historical data from a commercial building in Singapore are used to demonstrate the effectiveness of the proposed EMS.

Matching of air conditioning power and PV panel output power based on dynamic controlling of air conditioning air supply volume

ABSTRACT This paper presents a dynamic control strategy of air-conditioning air supply volume based on statistical data of the spatial and temporal distribution of occupants in the building, aiming at reducing overheating in buildings under local heat island effect. Nowadays, energy consumption in air-conditioning system is relatively high when considering the limited number of occupants and chiller plants operating inefficiently because the traditional central air-conditioning system was not designed to operate at different cooling load demands. Therefore, variable air volume (VAV) air-conditioning system is applied in this paper with corresponding wind volume control method. To address the above issue, in this paper: (1) A spatiotemporal distribution of occupants in the building is analyzed, and the relationship between occupant distribution and characteristics of the thermal dissipating load is established; (2) A strategy for dynamic controlling of the air supply volume in time and space is proposed; (3) A distributed photovoltaic system supplies power to cooling load of the building, and a strategy is proposed for optimizing the match between cooling power and photovoltaic (PV) output power using competitive swarm optimization algorithm; and (4) Air blower total amount of wind control method is adopted in VAV air-conditioning system for dynamic controlling of the air supply volume. This paper uses a simulation to verify the feasibility and optimality of the above strategies. The simulation results show that the building energy consumption is greatly reduced by 39.52%, the PV accommodation is improved as 77.14%, and four control indicators are well satisfied due to the air blower total amount of wind control method.

Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management

In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are – multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation (R); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model's performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction.

Optimization on ejector key geometries of a two-stage ejector-based multi-evaporator refrigeration system

A three-evaporator and dual-ejector refrigeration system was presented in this paper. By combining two single ejectors as a whole, the authors attempted to discuss the effects of key geometric dimensions of each stage on both stage performances with two-dimensional CFD simulation method. First, the CFD models were validated by experimental data. Then, the primary nozzle diameters of both stages were determined by the cooling load demands of the refrigerating and air-conditioning chambers. Next, the detailed effects of key geometric parameters such as the length of constant-pressure mixing section, the length of constant-area mixing section and area ratio of each stage on two-stage performances were identified with simulations. Finally, the optimum values of the key geometric parameters were obtained, and the results showed that the area ratio had the most significant influences on the entrainment ratio of both stages as compared to other parameters.

Water source heat pump energy demand prognosticate using disparate data-mining based approaches

This paper examines the data-mining and supervised based machine learning models for predicting 1-month ahead cooling load demand of an office building, including the primitive intention of enhancing the forecasting performance and the accuracy. The data-mining and supervised based machine learning models include; regression support vector machine, Gaussian process regression, scaled conjugate gradient, tree bagger, boosted tree, bagged tree, neural network, multiple linear regression and bayesian regularization. The external climate data, hours/day in a week, previous week load, previous day load and previous 24-h average load are applied as input parameters for these models. Whereas, the output of the models is the electrical power required for water source heat pump. A water source heat pump located in Beijing, China, is selected for examining 1-month ahead cooling load forecasting, i.e., from July 8 to August 7, 2016. In this paper, simulations are classified into three sessions: 7-days, 14-days and 1-month. The forecast performance is assessed by computing four performance indices such as mean square error, mean absolute error, root mean square error and mean absolute percentage error. The mean absolute percentage error for 7-days ahead cooling load prediction of the water source heat pump from data-mining based models, Gaussian process regression, tree bagger, boosted tree, bagged tree and multiple linear regression were 0.405%, 3.544%, 1.928%, 1.703% and 13.053% respectively. While, mean absolute percentage error of 7-days ahead forecasting in case of machine learning based models such as a regression support vector machine, Bayesian regularization, scaled conjugate gradient and neural network were 12.761%, 2.314%, 6.314%, 2.592% respectively. The percentage forecasting error index proved that the results of data-mining based models are more precise and similar to the existing machine learning models. The results also demonstrate that the better performance and efficiency in foreseeing the abnormal behaviour in forecasting and future cooling load demand in the building environment.

Optimization of night mechanical ventilation strategy in summer for cooling energy saving based on inverse problem method

Night ventilation has great energy saving potentials for public buildings in summer. However, night mechanical ventilation inevitably causes more fan energy consumptions, even though it can reduce the cooling loads for the next day and save the air conditioning energy consumption correspondingly. Thus the optimization of night mechanical ventilation strategy is highly significant for building energy efficiency. In this paper, the simplified two-plate room model is used and the optimization model of the mechanical ventilation and air conditioning system is established. To minimize the total energy consumption, the optimal night ventilation strategy is determined through inverse problem method. Taking an office room in Beijing as the illustrative example, the results show that the optimal air exchange rate for night mechanical ventilation is much higher than the traditional value (ACH = 0.5 h−1) and it highly depends on the indoor-outdoor air temperature difference and cooling load demands. Moreover, the total electricity usage amount can decline from 210 kWh to 111 kWh after optimization, reduced by about 47% in the whole summer season. It also indicates that the energy saving effect of night ventilation mainly derives from the cooling storage and load shifting of building internal walls. Compared to traditional ventilation strategy, the energy saving ratio increases with increasing thermal conductivity and specific heat of building internal walls. This work can provide guidance for practical ventilation system optimization design and operation strategy determination.

The influence of building using function on the operating characteristics of the gas engine driven heat pump with energy storage system (ESGEHPs)

The objective of this study is to improve the applicability and efficiency of the gas engine driven heat pump with energy storage system when supplying heating and cooling to the different types of buildings. The physical models of four types of buildings, including residential, hotel, office and university building, were sampled and simulated by the DesignBuilder software using the weather data of typical weather year in Tianjin. The building cooling and heating load characteristics of the typical air-conditioning day (17/07/2016) and the typical heating day (07/01/2017) was chosen as the research data. It was found that the four types of buildings had different load demand due to the different function and characteristics of the buildings. The control strategy of the gas engine driven heat pump with energy storage system with four modes, such as Mode L, Mode P, Mode F and Mode O, was developed to meet the various building heating and cooling load demand. The simulation results showed that the annual primary energy ratio of the gas engine driven heat pump with energy storage system is about 21.4% (residential), 35.2% (hotel), 23% (office)and 26.6% (university) higher than traditional gas engine heat pump system. And a compared experiment was given, which demonstrated that the simulation relative errors of the university building cooling load, the cooling/heating capacity of the gas engine driven heat pump with energy storage system, fuel-consumption and primary energy ratio were lower than 12%, except the heating load with the relative error of 35.6%. From these results, the gas engine driven heat pump with energy storage system could be used in different types of buildings with a more stable operation characteristic, higher primary energy ratio and more suitable performance. The results presented in this paper is of some reference basis for optimizing the gas engine driven heat pump with energy storage system and developing a new route for raising cooling/heating quality, energy saving and consumption reducing.

Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches

This paper depicted the novel data mining based methods that consist of six models for predicting accurate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment. The six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consumption of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The mean absolute percentage error results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression, bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment.

A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin

Abstract Rational scheduling strategy is the key to play the economy of district heating and cooling (DHC) system. However, very limited studies have been conducted on it using a suitable model or based on actual operational data, which made the obtained results not realistic or feasible. This paper proposes an actual operational-based optimal scheduling strategy to minimize the daily operation cost of an energy station in Tianjin integrated with electric chiller (EC) system, ground source heat pump (GSHP) system, water thermal energy storage (WTES) system and combined cooling, heating and power (CCHP) system under background of actual cooling load demand. Considering both nonlinear input-output characteristics and discrete working ranges of energy equipments, the mixed-integer nonlinear programming model is used to solve this problem. Results illustrate that the proposed optimal scheduling strategy can achieve zero waste of cooling energy and the cost saving ratio can reach to 24.3%, 34.2%, 47.3% and 63.9% under the load ratio of 75%, 60%, 45% and 30% respectively, compared with the existing scheduling strategy, which shows the cost saving effect is significant especially for the new-built energy station during the initial operation stage.

Improving prediction accuracy of cooling load using EMD, PSR and RBFNN

To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.

Energy-efficient slum house using alternative materials

The development of permanent energy-efficient houses for ever-increasing urban slum dwellers using sustainable construction materials is a major challenge in developing countries such as India. The exposed building surfaces (walls and roof) play a significant role in controlling the peak cooling load demand in climatic zones where median temperatures are high. This study examines the baseline building model performance for peak cooling load demand using the traditional construction materials for walls (fly ash bricks) and roof (reinforced cement concrete (RCC)). As approved by standards and local government practice, the building plan for one-, two- and three-storied structures was modelled in Revit software. With the aim to achieve energy efficiency in a cost-effective manner over the base case model, bio-fuel ash cellular lightweight (BFA-CLW) bricks and expanded polystyrene geofoam (EPGF) as an insulator were analysed. Results indicate that the model with BFA-CLW bricks and an EPGF-RCC roof is energy efficient, cost competitive and would conserve peak cooling load as compared with the base case building model. It can be concluded that to enhance the energy efficiency of urban slum houses, the application of locally available novel sustainable products and a computational approach for faster design decisions should be practised.

Towards innovative district energy management: a case study with stochastic renewable generators

The conversion of ocean wave power into sustainable electrical power represents a major opportunity to Nations endowed with such a kind of resource. At the present time the most of the technological innovations aiming at converting such resources are at early stage of development, with only a handful of devices close to be at the commercial demonstration stage. The Seaspoon device, thought as a large energy harvester, catches the kinetic energy of ocean waves with promising conversion efficiency, and robust technology, according to specific “wave-motion climate†. University of Genoa developed and patented a prototype to be deployed in medium average energy content seas (i.e. Mediterranean or Eastern Asia seas). This paper presents the installation phases of the first real scale prototype installed in the gulf of Genova and the monitoring of its performances. A brief description of the Seaspoon WEC is presented together with the monitoring equipment and procedures.In this research a thermoeconomic analysis of its integration in a real polygenerative district is also investigated. The impact of such this kind of stochastic renewable generator in the Savona Campus Smart Polygeneration Microgrid (SPM) is evaluated. The SPM plant is made up by (i) two auxiliary boilers (500kWth each), (ii) four micro gas turbines (30kWe, 2x65kWe and 100kWe), (iii) an internal combustion engine fed by natural gas (20kWe), (iv) an absorption chiller (100 kWf) and (v) PV panels for a total power installed of 100 kWe. Generators are “distributed†around the campus and they are coupled to electrical and thermal storages. Since the system is constituted by co-generative prime movers it can supply both electrical and thermal energy of the campus and the integration of storage is really important in order to follow both the requests, pursuing the best management strategy. The analysis of this smart-grid is performed exploiting a software developed by the Author's research group, which allows for the thermo-economic optimization of poly-generative energy systems. A model of the real plant was built and it was implemented in the software. The off-design curves of the real devices installed in the campus were used in order to increase the reliability of the simulation results. The grid was simulated considering the time dependent nature of the demands throughout the whole year. The model was used to simulate the smart grid behavior during the whole year, and find the best operational strategy. A time-dependent thermo-economic hierarchical approach has been used, considering the time-dependent electrical, thermal and cooling load demands during the year as problem constraints. The results are presented and discussed in depth and show the strong interaction between fossil and renewable resources, particularly the impact of unpredictable and randomized generators like the WECs ones. A dedicated model of the Seaspoon was implemented and exploited in the code.

A hybrid modeling approach for steady-state optimal operation of vapor compression refrigeration cycles

This paper presents a steady-state hybrid modeling approach for vapor compression refrigeration cycles which is intended to achieve an optimal system operation from an energy consumption point of view. The model development is based on a static characterization of the main components of the cycle using a hybrid approach, and their integration in a new optimization block. This block allows to determine completely the system stationary state by means of a non-linear optimization procedure subjected to several constraints such as mechanical limitations, component interactions, environmental conditions and cooling load demand. The proposed method has been tested in an experimental pilot plant with good results. Model validation for each identified hybrid model is carried out from a set of experimental data of 82 stationary operating points, with prediction errors below ±10%. The model is also globally validated by comparing experimental and simulated data, with a global mean relative absolute error less than 5%. The basic control structure consists of three decentralized control loops where the controller variables are the secondary fluid temperature at the evaporator inlet, the superheat, and the condenser pressure. While the secondary temperature is assumed as an imposed requirement, the optimal set-points of the other two control loops are searched offline using the proposed refrigerant cycle model. This set-point optimality is defined according to the coefficient of performance for minimizing the total electrical power consumption of the system at steady-state. This energy saving has been confirmed experimentally. The proposed method can be easily adapted for different sets of controlled variables in case of modification of the basic control structure. Furthermore, other energy efficiency metrics can be handily adopted. Considering the tradeoff between the accuracy and computational cost of the hybrid models, the proposed procedure is expected to be used in real-time applications.

Impact of Solar Reflectance Attenuation and Roof Cleaning on a Cool Roof: Assessing Return on Investment for Facility Management

ABSTRACT Research has shown roofing systems with high solar reflectance and thermal emissivity lead to less heat absorption, a consequential reduction in cooling load demand, and a resultant reduction on energy expenditure. Studies on energy savings from cool roof coatings have been conducted for decades and when compared to more traditional roofing systems have demonstrated energy savings ranging from 2-40%, with average savings estimated at 20%. The 20% average is widely used by cool roof industry professionals, designers, and contractors to market and sell the technology in the commercial sector to owners and owner representatives researching new roofs. While the 20% energy savings is a documented average, unfortunately there is no average roof. Each roof is unique considering size, materials, and location to name a few. In addition, the ability of the cool roof to maintain the original solar reflectance is integral to realizing energy savings. The case study calculated project payback for a 20-year cool roof design using both 30% and 20% estimated annual energy savings. In addition, building material specifications and solar reflectance attenuation in respect to reductions in cooling energy were projected into the payback calculations. Lastly, the cost impact of cleaning maintenance was added to the calculations to provide an analysis on affect to anticipated payback schedules. The results showed cleaning costs only added 1 year to project paybacks and saved over 262,244 kWh over 20 years.

Experimental and numerical study of thin ring and annular fin effects on improving the ice formation in ice-on-coil thermal storage systems

Cooling load demand during the warmest hours of summer days leads to an increase in electrical energy request, intensifying the peak load. Thus, to reduce electrical energy consumption in peak hours, cold energy storage systems are used. One of the most common methods in this area is ice on coil energy storage that deals with the problem of heat transfer rate decreasing during charging. In this paper, performance of two heat transfer enhancement methods including usage of thin rings and annular fins around coils are compared. First, the thermal behavior of the system is simulated and the sensitivity analysis is performed. Then, design parameters for experimental set up are selected based on the results obtained from the sensitivity analysis. For the case of annular fins, results show that the optimum operating point is deduced when the distance between two adjacent fins becomes 50mm. According to the numerical results, the experimental tests are designed. It is shown that ice formation in the cases of using annular fins and rings will be correspondingly 21% and 34% higher with respect to the bare tube. Besides, freezing will be sped up by 15% in the case of using finned and ringed tubes. Accordingly, diagonal thin rings have better performance with respect to annular fins. The reason is that the ice formation rate in the central region between the tubes is enhanced due to the presence of thin rings in this region.

Transforming typical hourly simulation weather data files to represent urban locations by using a 3D urban unit representation with micro-climate simulations

Urban and building energy simulation models are usually driven by typical meteorological year (TMY) weather data often in a TMY2 or EPW format. However, the locations where these historical datasets were collected (usually airports) generally do not represent the local, site specific micro-climates that cities develop. In this paper, a humid sub-tropical climate context has been considered. An idealised “urban unit model” of 250 m radius is being presented as a method of adapting commonly available weather data files to the local micro-climate. This idealised “urban unit model” is based on the main thermal and morphological characteristics of nine sites with residential/institutional (university) use in Hangzhou, China. The area of the urban unit was determined by the region of influence on the air temperature signal at the centre of the unit. Air temperature and relative humidity were monitored and the characteristics of the surroundings assessed (eg green-space, blue-space, built form). The “urban unit model” was then implemented into micro-climatic simulations using a Computational Fluid Dynamics – Surface Energy Balance analysis tool (ENVI-met, Version 4). The “urban unit model” approach used here in the simulations delivered results with performance evaluation indices comparable to previously published work (for air temperature; RMSE 0.9). The micro-climatic simulation results were then used to adapt the air temperature and relative humidity of the TMY file for Hangzhou to represent the local, site specific morphology under three different weather forcing cases, (ie cloudy/rainy weather (Group 1), clear sky, average weather conditions (Group 2) and clear sky, hot weather (Group 3)). Following model validation, two scenarios (domestic and non-domestic building use) were developed to assess building heating and cooling loads against the business as usual case of using typical meteorological year data files. The final “urban weather projections” obtained from the simulations with the “urban unit model” were used to compare the degree days amongst the reference TMY file, the TMY file with a bulk UHI offset and the TMY file adapted for the site-specific micro-climate (TMY-UWP). The comparison shows that Heating Degree Days (HDD) of the TMY file (1598 days) decreased by 6 % in the “TMY + UHI” case and 13 % in the “TMY-UWP” case showing that the local specific micro-climate is attributed with an additional 7 % (ie from 6 to 13 %) reduction in relation to the bulk UHI effect in the city. The Cooling Degree Days (CDD) from the “TMY + UHI” file are 17 % more than the reference TMY (207 days) and the use of the “TMY-UWP” file results to an additional 14 % increase in comparison with the “TMY + UHI” file (ie from 17 to 31 %). This difference between the TMY-UWP and the TMY + UHI files is a reflection of the thermal characteristics of the specific urban morphology of the studied sites compared to the wider city. A dynamic thermal simulation tool (TRNSYS) was used to calculate the heating and cooling load demand change in a domestic and a non-domestic building scenario. The heating and cooling loads calculated with the adapted TMY-UWP file show that in both scenarios there is an increase by approximately 20 % of the cooling load and a 20 % decrease of the heating load. If typical COP values for a reversible air-conditioning system are 2.0 for heating and 3.5 for cooling then the total electricity consumption estimated with the use of the “urbanised” TMY-UWP file will be decreased by 11 % in comparison with the “business as usual” (ie reference TMY) case. Overall, it was found that the proposed method is appropriate for urban and building energy performance simulations in humid sub-tropical climate cities such as Hangzhou, addressing some of the shortfalls of current simulation weather data sets such as the TMY.

Modelling commercial refrigeration systems coupled with water storage to improve energy efficiency and perform heat recovery

A basic CO2 transcritical/subcritical commercial refrigeration system is considered, applied to cold rooms and display cabinets in a supermarket. Subcooling of the refrigerant or heat recovery from condensation can be performed, taking advantage of a large fire prevention water tank. The whole refrigeration system is modelled in a TRNSYS environment, taking into account the hourly weather data and calculating the hourly cooling load demand from display cabinets and cold rooms equipment. New types have been written to describe display cabinets and cold rooms, CO2 refrigerating units and a particular water store.Simulations consider a simple double compression cycle with liquid receiver, and other options among which an auxiliary compressor. Results show that CO2 plants are feasible and energetically acceptable in mild climates, provided that improvements to standard cycle are adopted. Furthermore, heat recovery can be effectively performed through the employment of a heat storage.

Cool black roof impacts into the cooling and heating load demand of a residential building in various climates

This paper describes the impacts of different cool black paints on cooling and heating load demand of a residential building in different climates. Cool paints with dark appearance and high reflectance in near infrared portion of sunlight are of great interest by the owners of homes to satisfy both desired visual and energy saving aspects. Three copper oxide (CuO) powders, different in pigment size, and a chromium–iron oxide (Cr2O3–Fe2O3) powder, as a commercial cool black pigment, are used to make the black paints. The samples are made on both conventional black and cool white base coats. The spectral reflectance of all samples in solar range (0.3–2.5µm) and infrared region (2.5–25µm) is measured. The thermal emittance of the samples is estimated using the spectral reflectance measurement and validated by the measurement of infrared camera. A typical residential single-stage single-family home in three different climates of Iran is considered and the impacts of applying the different black paints on cooling and heating load demand are discussed. The results show that the peak and annual cooling load demand decrease by 0.4–1.4kW (6–13%) and 405–2202kWh (6–14%), respectively, depends on the type of pigmented coating and climate conditions.

Economic feasibility of trigeneration plants for various prime movers and triple load demands

In this paper, after thermal and economic modeling of cogeneration plant, this system is optimized to find the optimal prime mover and their benefit for various cooling, heating, and electrical demand loads. To find the optimal prime mover and their benefit for each triple load, two new nondimensional design parameters including electric cooling ratio and nominal power ratio are defined. It is observed that, for example, for higher electrical and lower heating load demands, the gas engine is more profitable while for higher electrical and heating load demands, diesel engine is more profitable. In addition, some ranges of demand loads at which using CCHP plant is not profitable (in comparison with traditional system) are also obtained and presented. The optimum results obtained in NO SELL mode show that the highest values of actual annual benefit (AAB) are obtained for highest values of electrical load demand. This region corresponds with values of Hdmn/ Qdmn (heating to cooling load demand ratio) in the range of 1.5–3.5. The highest values of AAB for SELL mode are obtained to be in the range of 0.5–3.5 for Hdmn/ Qdmn (heating to cooling load demand ratio).

Model-based feedback control of an ammonia-water absorption chiller

In this study, potential control strategies for a small-scale, 3.5-kW ammonia–water absorption chiller are developed and investigated numerically using a dynamic simulation model. The dynamic model is employed to study the response of the system under varying ambient temperatures and cooling load demands. Two strategies are investigated for feedback control of the system. The first control loop maintains the evaporator temperature glide set-point (Tevap,out – Tevap,in), while the second control loop regulates desorption temperature to provide the desired cooling duty and maintain the delivered coolant temperature at a set-point. At design operating conditions, the proposed control for evaporator temperature glide requires ∼250 s to reach the set-point of 3°C, starting from ambient conditions. With the proposed implementation of desorption temperature control, the system can respond to changes in ambient temperature and cooling load demand, and maintain the delivered coolant temperature within ±0.75°C of the set-point. At part-load operation, the proposed techniques can increase the system coefficient of performance by 8% or greater.