• Energy Research

This paper evaluates the energy and cost benefits for deployment of hybrid indirect evaporative and vapor compression systems for the existing Saudi residential building stock. Both experimental and simulation analyses are carried out to perform large scale energy, environmental, and economic assessments. The energy efficiency of the hybrid system is tested and measured under various operating conditions. The experimental data are then incorporated in a residential building stock model established for Saudi Arabia to quantify the energy, environmental, and economic performance of the hybrid systems relative to the current air conditioning systems. It is found through the testing analysis that the hybrid systems can double of the coefficient of performance of the vapor compression only air conditioning units especially when outdoor air temperatures are high. Moreover, it is estimated that the deployment of hybrid systems for Saudi residential building stocks achieves annual reductions of 51 TWh in electricity consumption and 38 million tons in carbon emissions with an average payback period of less than one year. ; The study presented in this paper is funded by KAUST Cooling Initiative, REP/3988/01-01.

This paper evaluates the energy and cost benefits for deployment of hybrid indirect evaporative and vapor compression systems for the existing Saudi residential building stock. Both experimental and simulation analyses are carried out to perform large scale energy, environmental, and economic assessments. The energy efficiency of the hybrid system is tested and measured under various operating conditions. The experimental data are then incorporated in a residential building stock model established for Saudi Arabia to quantify the energy, environmental, and economic performance of the hybrid systems relative to the current air conditioning systems. It is found through the testing analysis that the hybrid systems can double of the coefficient of performance of the vapor compression only air conditioning units especially when outdoor air temperatures are high. Moreover, it is estimated that the deployment of hybrid systems for Saudi residential building stocks achieves annual reductions of 51 TWh in electricity consumption and 38 million tons in carbon emissions with an average payback period of less than one year. ; The study presented in this paper is funded by KAUST Cooling Initiative, REP/3988/01-01.

Abstract Owing to the diverse photovoltaic (PV) systems’ design and technology, as well as the dynamic nature of insolation data received on the aperture surfaces, the instantaneous output from a PV system fluctuates greatly. For accurate performance estimation of a large PV field, the long term performance as electrical output is a more rational approach over the conventional testing methods, such as at Standard Testing Conditions (STC) and at the Nominal Operating Cell Temperature (NOCT) available hitherto. In this paper, the long-term performances of concentrated PVs (Cassegrain reflectors and Fresnel lens) with 2-axes tracking and a variety of PV systems, namely the stationary flat-plate PV (mono-crystalline, poly-crystalline and thin-films CIS types), is presented over a period of one year for the merit comparison of system design, under the tropical weather conditions of Singapore. From the measured field performances, the total energy output of 240.2 kW h/m 2 /year is recorded for CPV operation in Singapore, which is nearly two folds higher than the stationary PV panels.

Abstract Owing to the diverse photovoltaic (PV) systems’ design and technology, as well as the dynamic nature of insolation data received on the aperture surfaces, the instantaneous output from a PV system fluctuates greatly. For accurate performance estimation of a large PV field, the long term performance as electrical output is a more rational approach over the conventional testing methods, such as at Standard Testing Conditions (STC) and at the Nominal Operating Cell Temperature (NOCT) available hitherto. In this paper, the long-term performances of concentrated PVs (Cassegrain reflectors and Fresnel lens) with 2-axes tracking and a variety of PV systems, namely the stationary flat-plate PV (mono-crystalline, poly-crystalline and thin-films CIS types), is presented over a period of one year for the merit comparison of system design, under the tropical weather conditions of Singapore. From the measured field performances, the total energy output of 240.2 kW h/m 2 /year is recorded for CPV operation in Singapore, which is nearly two folds higher than the stationary PV panels.

A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant's operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant's operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.

A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant's operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant's operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.