Assessment of the technical potential for multifunction building zero-carbon renovation with EnergyPlus

Energy consumed in the building field accounts for 30-40% of social energy consumption in the entire world. The energy used in office-educational buildings, especially in developed countries, takes up a large proportion, because of the high occupant density, long running time and high brightness and comfort requirement. This research aims at predicting the renovation potential for office-educational building in the energy-saving aspect. One typical existing educational building in the UK has been chosen as the research object; EnergyPlus computer model, which accurately reflects the building style, material, structures and Heating, Ventilation and Air Conditioning system is established. After the simulation of energy performance and carbon emission of the original building, some insulation measures and renewable technologies (photovoltaics, solar thermal etc.) have been added as the renovation method. Then, theoretical calculation and computer simulation about the effect of the renovations above have been conducted to reflect the improvement. Carbon-saving effects of renovation methods were compared and analyzed. Only 2.2% of carbon emissions can be reduced by improving the air-tightness of the building and the U-value of windows, but great carbon saving achieved by adding renewable devices. About 40.8% of the carbon emissions reduced due to the application of all the renovation methods. The result of this study will provide some critical references for the choosing and prediction of renovation methods in the energy-saving field in the UK. © The Author 2012. Published by Oxford University Press

Development of a novel designs and assessment and selection system for green office buildings in China

China is facing severe problems in fossil fuel consumption and pollutant emission largely owing to the construction of buildings. Office buildings, as a major building type in China, contribute around 22% of the national fossil fuel energy use and 14% of carbon emissions. The identification of the most appropriate solution to energy saving and pollutant emissions reducing at the earlier design stage of office buildings is significantly important to China’s sustainability development and environmental protection.This PhD research aims to establish a simple and straight-forward assessment method that can predict fossil fuel energy use and the associated pollutant emissions of the Chinese office buildings at their early conceptual design stage (when the detailed material and constructional information is unavailable), and further, develop a computer- aided assessment and selection process that can identify the best design solution to the office buildings of China. This work is carried out through a standard research process including literature review, methodology development, computer model establishment, case study and results analysis with comparisons, followed by recommendations. As a result, the research provides a variety of important outputs, i.e., the life-cycle energy and air-pollutants estimation method, the generalized environmental impact metric system, and the green office building design solution assessment and selection system (GBAS). It has been demonstrated that the simplified energy and pollutants estimation method can predict the energy consumption and associated pollutant emissions at each office building life-cycle phase, based on the refined mathematical correlations and associated computerized toolkits. By using the generalized environmental impact metric system, the pollutant equivalent (PE), which reflects the combined environmental impact of the emission of four common pollutants, is derived and its values are discussed in detail. Based on the estimation of life-cycle energy and PE, the GBAS system is developed to identify the “best” design solution on both the quantitative survey and qualitative analyses.A combination of all the above outcomes leads to the development of a comprehensive computerized tool that can conduct faster assessment, optimization and selection of the “best” design solutions for Chinese office buildings at their very earlier stage of design. The prediction results have been proven to be rational, realistic and applicable to practical engineering projects.The outcomes of the research can help in the design of energy efficient and “green” office buildings in China, thus contributing to China’s sustainable development and environmental protection

Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance

Real life test of a novel super performance dew point cooling system in operational live data centre

This paper presents the development and application of a super performance dew point cooling technology for data centres. The novel super performance dew point cooler showed considerably improved energy saving and carbon reduction for data centre cooling. The innovations of this technology are built upon a series of technological breakthroughs including, a novel hybrid flat/corrugated heat and mass exchanging sheets, an innovative highly water absorptive and diffusive wet-material for the sheets which enable an intermittent water supply with well-tuned water pressure and flow rate, and the optimised fan configurations. Following a list of fundamental research including theoretical, numerical and lab experimental testing of a small scale prototype system, a specialist 100 kW rated data centre dew point cooling system was dedicated designed, constructed, installed and real life tested in an operational live data centre environment, i.e., Maritime Data Centre at Hull (UK) to investigate its dynamic performance, suitability and stability for application in operational data centre environment conditions. During the testing period, the system showed its reliability and capability to remove a tremendous amount of heat dissipated from the IT equipment and maintain an adequate space temperature in the operational live data centre. The dynamic data collection and analysis during the continuous testing and monitoring period showed the average COP of 29.7 with the maximum COP of 48.3. Compared to the existing traditional vapor compression air conditioning system in the data centre, the energy saving using the super performance dew point cooling system is around 90 %. The work presented in this paper include detailed innovation aspects of the technology and the system operation, as well as the established bridging knowledges, methodology and technical procedure for bringing this new technology into real life operation which involve in data centre survey, optimum design and modularization of the specialist cooling system for data centre application, proven system installation, operating method and cooling air management for data centre as well as the assurance of the continuous sufficient cooling supply to the data centre

Operational performance of a novel fast-responsive heat storage/exchanging unit (HSEU) for solar heating systems

In order for a solar heating system to provide heat immediately after sunrise, a fast response is needed to the heat demand of a serviced space. The majority of existing solar heating systems have a slow response time due to the large volume of water stored in the heat storage/exchanger unit (HSEU). This leads to a slow heat delivery cycle, which results in discomfort for the occupants and thus creates a huge barrier to the wide deployment of solar heating systems. To overcome this critical issue, a novel interactive heat storage/exchanging unit (HSEU) employing a double-tank configuration was developed. Unlike conventional HSEUs, which have a single tank acting as the heat storage and exchanging unit, the new HSEU is comprised of a small tank for heat exchange combined with a large tank for heat storage. The small tank enables fast transfer of solar heat to the heating loop fluid without having to heat up the large volume of water in the entire HSEU tank, whilst the large tank is used to store and exchange heat between itself and the small tank using a temperature-oriented control mechanism. To test the proposed design, the heat transfer between the first (solar loop) and second (heating loop) fluids, the heat and mass transfer between the small and large tank and the associated operational strategy were investigated experimentally and theoretically for comparison. A conventional single tank HSEU requires around 120 min to deliver heat to a served space, whilst the new interactive double-tank HSEU can provide heat to the served space in around 20 min, thus creating a heating system which can respond significantly faster than traditional systems. The investigation of the heat exchange effect between the solar and heating loop fluids showed that the new HSEU achieved a convective heat transfer coefficient of as high as 391 W/m2·K, which is 551% higher than that of a conventional tank. As a result, the solar thermal efficiency of the solar panel-array in the new HSEU based system was increased by 7.5% compared to conventional HSEU based systems

Modular Solar System for Building Integration

© 2019, Springer Nature Switzerland AG. Building integrated solar systems, which means components of solar thermal collectors and/or solar photovoltaics (PV) are completely integrated with building envelopes, can potentially provide additional functions of on-site hot water and electric generation over the building envelopes’ basic functions, i.e. weatherproof and thermal insulation. This is different from the conventional approach of applying solar systems into buildings. Traditionally, end-users purchase and install solar thermal collectors and/or PV panels on a building’s roof or façade according to their own requirements after the completion of building construction. Potential risks of the conventional approach, such as that the appearance and internal structure of the building envelope can be damaged, seriously restrict the development and implementation of solar technologies’ building application. Building integrated solar systems can eliminate these risks by taking the advantage of using areas of building envelopes (i.e. walls, roofs and windows) for immediate solar energy capture and conservation. In so doing, it can effectively reduce the construction time and cost, and enhance building envelopes’ security. In addition, some latest building integrated solar systems will also improve the thermal performance of building envelope and thus reduce building heating and cooling demands. This chapter mainly introduces how to integrate solar systems into building envelopes, and thus provides a reference for achieving effective and efficient utilization of solar energy in buildings and improving the prediction and optimization of building energy demands

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability

Optimal Decision-Making Model of Agricultural Product Information Based on Three-Way Decision Theory

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability

Theoretical and experimental study of a novel solar indirect-expansion heat pump system employing mini channel PV/T and thermal panels

This paper presents the investigation of a novel mini channel PV/T and thermal collectors combined heat pump system, using both experimental and theoretical methods. Data were produced under conditions typical for winter days in Lvliang, China. A simulation model is developed to conduct theoretical evaluation based on real-world conditions. The experimental and simulated electrical and thermal efficiency of PV/T panels, thermal efficiency of thermal collectors and COP of heat pump are compared. It is shown that the experimental and simulated results are in close agreement. The errors range from 4.0% to 9.1%, giving us confidence that this model is reasonable to predict the seasonal performance of the system. The experimental and simulated results of the system provide fundamental data for performance analysis in winter conditions and inform further improvements of similar systems in the future