A Resampling Slack-Based Energy Efficiency Analysis: Application in the G20 Economies

In order to have a sustainable economic and social development, it is important to balance economic growth and ecological environmental damage. In this article, we used the resampling model under the triangular distribution to evaluate energy efficiency, because the input/output value may have measurement errors, time lag factors, arbitrariness, and other problems, causing their own DMU to change. After these factors were taken into consideration, the resampled input/output was estimated because a super-SBM efficiency value was placed in the confidence interval. From the past-present data, for the estimated data change, the time weight was provided according to the Lucas series, and the super-SBM was time-weighted. We applied this model to a dataset of G20 economies from 2010 to 2014. To the best of our knowledge, very few studies have applied the DEA method with resampling to analyze energy efficiency. Thus, our study contributes to the methodologies for energy efficiency evaluation. We found that the overall average energy efficiency is 0.653, with substantial differences between developed economies and developing economies. The most important finding is that neither overestimation nor underestimation occurred when sampling was repeated one thousand times using 95% and 80% confidence intervals, confirming the robustness of the super-SBM model. The less energy-efficient economies should adjust their energy policies appropriately and develop new clean energy technologies in the future.

User Deep Energy Efficiency Evaluation via Load Perception and Clustering

With the constantly upgrading of the living level of urban residents, the types and quantities of domestic appliances continue to increase, and the electricity consumption behaviour is becoming more and more complex. Based on the fine-grained user energy consumption data obtained by load intelligent perception, this paper proposes a user energy efficiency evaluation method through fuzzy comprehension evaluation. First, the energy consumption characteristics at appliance level which can effectively represent energy consumption behaviour of users are designed and filtered, and then the energy efficiency evaluation strategy of appliances based on fuzzy comprehensive evaluation is established. The strategy adopts two-level fuzzy comprehensive evaluation. The first appliance-level fuzzy evaluation realizes the energy efficiency evaluation of the appliances. The second level is the comprehensive evaluation of user energy efficiency based on the appliance energy efficiency assessment, and the target users are divided into different energy efficiency levels. The measured data-based analysis results prove the effectiveness of the proposed method, and the results can guide users to optimize their electricity consumption behaviour scientifically and realize energy saving and carbon reduction.

Energy Consumption Analysis and Comprehensive Energy Efficiency Evaluation of Campus Central Heating System Based on Heat Supply Monitoring Platform

The energy conservation of campus buildings is the most direct and effective means to promote the sustainable development of colleges and universities. Using the heat data collected by heat supply monitoring platform to perform real-time energy consumption analysis and heating energy efficiency evaluation is a prerequisite for realizing automatic heat supply control based on energy conservation and heating demand. This paper studied the energy conservation analysis and comprehensive energy efficiency evaluation of campus central heating system based on the heat supply monitoring platform. At first, the paper elaborated on the energy consumption analysis method of the campus central heating system based on the heat supply monitoring platform; then, it gave the energy balance equation and the exergy balance equation; after that, based on a few parameters such as the EHR (electricity to heat ratio), heating index, and water makeup rate, this paper evaluated the energy efficiency of the campus central heating system, and experimental results verified the effectiveness of the analysis and evaluation methods.