• Energy Research

Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms’ mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.

Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms’ mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.

Increasing demand for biofuel production and global competition for the use of natural resources are key factors in finding new and environmentally safe routes for methanol production. In the present study, life cycle assessment was used to analyse the potential environmental impact and environmental cost of a novel methanol production process from wood compared to a conventional processes. Both the novel and the conventional process were divided into three stages: pre-treatment, gasification, and syngas cleaning and methanol synthesis. The environmental impacts were assessed and compared using Simapro 9 (ecoinvent 3.5 database) and the ReCiPe 2016 (World-H) midpoint method. The results, expressed per tonne methanol, showed that the impact categories of global warming potential (GWP) and marine ecotoxicity potential were lower in the novel process in comparison to the conventional process (48.2 kg CO2 eq. vs. 63.1 kg CO2 eq., and 4.55 kg 1,4-DCB vs. 6.35 kg 1,4-DCB respectively). However, the novel process had a higher environmental impact in the pre-treatment stage. The results of the sensitivity analysis showed that the GWP of the novel process increased from 48.2 kg CO2 eq. to 216 kg CO2 eq. due to the replacement of Na2CO3 by K2CO3. The human toxicity impact categories showed significant impact on environmental cost. These findings will help relevant industries to reduce their environmental impact and improve the production efficiency of methanol from wood.

Increasing demand for biofuel production and global competition for the use of natural resources are key factors in finding new and environmentally safe routes for methanol production. In the present study, life cycle assessment was used to analyse the potential environmental impact and environmental cost of a novel methanol production process from wood compared to a conventional processes. Both the novel and the conventional process were divided into three stages: pre-treatment, gasification, and syngas cleaning and methanol synthesis. The environmental impacts were assessed and compared using Simapro 9 (ecoinvent 3.5 database) and the ReCiPe 2016 (World-H) midpoint method. The results, expressed per tonne methanol, showed that the impact categories of global warming potential (GWP) and marine ecotoxicity potential were lower in the novel process in comparison to the conventional process (48.2 kg CO2 eq. vs. 63.1 kg CO2 eq., and 4.55 kg 1,4-DCB vs. 6.35 kg 1,4-DCB respectively). However, the novel process had a higher environmental impact in the pre-treatment stage. The results of the sensitivity analysis showed that the GWP of the novel process increased from 48.2 kg CO2 eq. to 216 kg CO2 eq. due to the replacement of Na2CO3 by K2CO3. The human toxicity impact categories showed significant impact on environmental cost. These findings will help relevant industries to reduce their environmental impact and improve the production efficiency of methanol from wood.

Cities in developing nations have to deal with two significant sustainability challenges amidst rampant urbanization. First, consumer-generated food waste is increasing monumentally since open dumping is still followed as a predominant practice, the negative environmental externalities associated with food waste disposal are growing beyond manageable proportions. Second, the dependency on conventional fuels like diesel to operate transit buses, which is one of the major causes for deteriorating urban air quality. A nexus established between food waste management and operation of transit buses can improve the sustainable performance of cities in developing nations. In this study, a Life Cycle Assessment (LCA) supported Social Cost-Benefit Analysis (SCBA) is performed by considering a hypothetical scenario of establishing a large food waste treating biomethanation plant in Mumbai, India. The food waste from the city is transported to a biomethanation plant where it is subjected to an anaerobic digestion (AD) process. The biogas produced as a byproduct is upgraded to compressed biomethane (CBM) and used as a vehicle fuel to operate transit buses within the city. The LCA results suggest that CBM buses can reduce greenhouse gas and particulate matter emissions by 60% compared to diesel or compressed natural gas (CNG) buses. Fossil depletion potential of CBM buses is 98% lower than diesel, suggesting CBM’s importance in decoupling developing nations dependency on imported crude oil. The SCBA considers: (a) costs to stakeholders, i.e., fees for open dumping of food waste and cost of fuel for operating transit buses; and (b) social costs incurred by negative environmental externalities (obtained by monetizing LCA results) resulting from both, open dumping as well as fuel combustion. SCBA results indicate that the food waste-based CBM model can save 6.86 billion Indian rupees (USD 99.4 million) annually for Mumbai. The savings are made due to a reduction in stakeholder’s costs (fuel) coupled with societal, i.e., environmental externality costs if entire transit bus fleet operates on CBM fuel instead of conventional fuel mix (33:67 diesel to CNG) currently used. Although the study is performed for Mumbai, the results will be replicable to any city of developing nations facing similar issues.

Cities in developing nations have to deal with two significant sustainability challenges amidst rampant urbanization. First, consumer-generated food waste is increasing monumentally since open dumping is still followed as a predominant practice, the negative environmental externalities associated with food waste disposal are growing beyond manageable proportions. Second, the dependency on conventional fuels like diesel to operate transit buses, which is one of the major causes for deteriorating urban air quality. A nexus established between food waste management and operation of transit buses can improve the sustainable performance of cities in developing nations. In this study, a Life Cycle Assessment (LCA) supported Social Cost-Benefit Analysis (SCBA) is performed by considering a hypothetical scenario of establishing a large food waste treating biomethanation plant in Mumbai, India. The food waste from the city is transported to a biomethanation plant where it is subjected to an anaerobic digestion (AD) process. The biogas produced as a byproduct is upgraded to compressed biomethane (CBM) and used as a vehicle fuel to operate transit buses within the city. The LCA results suggest that CBM buses can reduce greenhouse gas and particulate matter emissions by 60% compared to diesel or compressed natural gas (CNG) buses. Fossil depletion potential of CBM buses is 98% lower than diesel, suggesting CBM’s importance in decoupling developing nations dependency on imported crude oil. The SCBA considers: (a) costs to stakeholders, i.e., fees for open dumping of food waste and cost of fuel for operating transit buses; and (b) social costs incurred by negative environmental externalities (obtained by monetizing LCA results) resulting from both, open dumping as well as fuel combustion. SCBA results indicate that the food waste-based CBM model can save 6.86 billion Indian rupees (USD 99.4 million) annually for Mumbai. The savings are made due to a reduction in stakeholder’s costs (fuel) coupled with societal, i.e., environmental externality costs if entire transit bus fleet operates on CBM fuel instead of conventional fuel mix (33:67 diesel to CNG) currently used. Although the study is performed for Mumbai, the results will be replicable to any city of developing nations facing similar issues.