• Energy Research
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Abstract Building energy prediction is a key factor to assess the energy performance of commercial buildings, identify operation issues and propose better operating strategies based on the forecast information. Different models have been used to forecast energy demand in buildings, including whole building energy simulation, regression analysis, and black-box models (e.g., artificial neural networks). This paper presents a different approach to predict the energy demand of commercial buildings using case-based reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes, Quebec. The energy demand is predicted at every hour for the following 3 h using weather forecasts. The results show that during occupancy, 7:00–18:00, the coefficient of variance of the root-mean-square-error (CV-RMSE) is below 13.2%, the normalized mean bias error (NMBE) is below 5.8% and the root-mean-square-error (RMSE) is below 14 kW. When the statistical criteria are calculated for all hours of the day, the CV-RMSE is 12.1%, the NMBE is 1.0% and the RMSE is 11 kW. The case study demonstrates that CBR can be used for energy demand prediction and could be implemented in building operation systems.

Abstract Building energy prediction is a key factor to assess the energy performance of commercial buildings, identify operation issues and propose better operating strategies based on the forecast information. Different models have been used to forecast energy demand in buildings, including whole building energy simulation, regression analysis, and black-box models (e.g., artificial neural networks). This paper presents a different approach to predict the energy demand of commercial buildings using case-based reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes, Quebec. The energy demand is predicted at every hour for the following 3 h using weather forecasts. The results show that during occupancy, 7:00–18:00, the coefficient of variance of the root-mean-square-error (CV-RMSE) is below 13.2%, the normalized mean bias error (NMBE) is below 5.8% and the root-mean-square-error (RMSE) is below 14 kW. When the statistical criteria are calculated for all hours of the day, the CV-RMSE is 12.1%, the NMBE is 1.0% and the RMSE is 11 kW. The case study demonstrates that CBR can be used for energy demand prediction and could be implemented in building operation systems.

Carbon capture, utilization, and storage (CCUS) is a gas injection technology that enables the storage of CO2 underground. The aims are twofold, on one hand to reduce the emissions of CO2 into the atmosphere and on the other hand to increase oil/gas/heat recovery. Different types of CCUS technologies and related engineering projects have a long history of research and operation in the USA. However, in China they have a short development period ca. 10 years. Unlike CO2 capture and CO2-EOR technologies that are already operating on a commercial scale in China, research into other CCUS technologies is still in its infancy or at the pilot-scale. This paper first reviews the status and development of the different types of CCUS technologies and related engineering projects worldwide. Then it focuses on their developments in China in the last decade. The main research projects, international cooperation, and pilot-scale engineering projects in China are summarized and compared. Finally, the paper examines the challenges and prospects to be experienced through the industrialization of CCUS engineering projects in China. It can be concluded that the CCUS technologies have still large potential in China. It can only be unlocked by overcoming the technical and social challenges.

Carbon capture, utilization, and storage (CCUS) is a gas injection technology that enables the storage of CO2 underground. The aims are twofold, on one hand to reduce the emissions of CO2 into the atmosphere and on the other hand to increase oil/gas/heat recovery. Different types of CCUS technologies and related engineering projects have a long history of research and operation in the USA. However, in China they have a short development period ca. 10 years. Unlike CO2 capture and CO2-EOR technologies that are already operating on a commercial scale in China, research into other CCUS technologies is still in its infancy or at the pilot-scale. This paper first reviews the status and development of the different types of CCUS technologies and related engineering projects worldwide. Then it focuses on their developments in China in the last decade. The main research projects, international cooperation, and pilot-scale engineering projects in China are summarized and compared. Finally, the paper examines the challenges and prospects to be experienced through the industrialization of CCUS engineering projects in China. It can be concluded that the CCUS technologies have still large potential in China. It can only be unlocked by overcoming the technical and social challenges.

Abstract Eradicating poverty and mitigating greenhouse gas (GHG) emissions are core issues of global sustainable development goals (SDGs), and China is struggling in realizing these targets. The poverty reduction that leads to popualtion structure and lifestyle changes would have an impact on GHG emission changes. However, few studies have assessed the historical and future impacts of the poverty allevation on China's emissions. Here by linking Chinese Multi-Regional Input Output (MRIO) database to the global MRIO database EXIOBASE, and using provincial household consumption data, we identified the distribution of Chinese household greenhouse gas footprints (HGFs) by income groups in 2015 at the national and provinical levels. Moreover, we focused on the historical impact of poverty alleviation on HGFs during 2010–2015, and developed four scenarios to project future HGFs changes due to poverty alleviation by 2030. We find that eradicating extreme poverty in the secanrio S2, i.e., bringing people to an income above $1.9 daily, does not cause a large emission impact with current technological level. However, lifting people from a higher poverty line of $5.5 per day in the sceanrio S4 results in a 1.6% increase in emissions compared with the scenario S1 without any poverty reduction goals. Furthermore, realizing a higher poverty reduction target will result in an increase of emissions contribution from internatioanl supply chains due to the differences in consumption patterns among different income groups. Our study highlights the conflict between the high poverty alleviaition goal and emission reduciton in China, and reminds us of the need to make more technological efforts for avoiding the large emissions embodied in international supply chains.

Abstract Eradicating poverty and mitigating greenhouse gas (GHG) emissions are core issues of global sustainable development goals (SDGs), and China is struggling in realizing these targets. The poverty reduction that leads to popualtion structure and lifestyle changes would have an impact on GHG emission changes. However, few studies have assessed the historical and future impacts of the poverty allevation on China's emissions. Here by linking Chinese Multi-Regional Input Output (MRIO) database to the global MRIO database EXIOBASE, and using provincial household consumption data, we identified the distribution of Chinese household greenhouse gas footprints (HGFs) by income groups in 2015 at the national and provinical levels. Moreover, we focused on the historical impact of poverty alleviation on HGFs during 2010–2015, and developed four scenarios to project future HGFs changes due to poverty alleviation by 2030. We find that eradicating extreme poverty in the secanrio S2, i.e., bringing people to an income above $1.9 daily, does not cause a large emission impact with current technological level. However, lifting people from a higher poverty line of $5.5 per day in the sceanrio S4 results in a 1.6% increase in emissions compared with the scenario S1 without any poverty reduction goals. Furthermore, realizing a higher poverty reduction target will result in an increase of emissions contribution from internatioanl supply chains due to the differences in consumption patterns among different income groups. Our study highlights the conflict between the high poverty alleviaition goal and emission reduciton in China, and reminds us of the need to make more technological efforts for avoiding the large emissions embodied in international supply chains.

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.