Energy Informatics最新文献

The cooling systems contribute to 40% of overall building energy consumption. Out of which, 40% is wasted because of faulty parts that cause anomalies in the cooling systems. We propose a three-stage, non-invasive part-level anomaly detection technique to identify anomalies in both cooling systems, a ducted-centralized and a ductless-split. We use COTS sensors to monitor temperature and energy without invading the cooling system. After identifying the anomalies, we find the cause of the anomaly. Based on the anomaly, the solution recommends a fix. If there is a technical fault, our proposed technique informs the technician regarding the faulty part, reducing the cost and time needed to repair it. In the first stage, we propose a domain-inspired time-series statistical technique to identify anomalies in cooling systems. We observe an AUC-ROC score of more than 0.93 in simulation and experimentation. In the second stage, we propose using a rule-based technique to identify the cause of the anomaly. We classify causes of anomalies into three classes. We observe an AUC-ROC score of 1. Based on the anomaly classification, we identify the faulty part of the cooling system in the third stage. We use the Nearest-Neighbour Density-Based Spatial Clustering of Applications with Noise (NN-DBSCAN) algorithm with transfer learning capabilities to train the model only once, where it learns the domain knowledge using the simulated data. The trained model is used in different environmental scenarios with both types of cooling systems. The proposed algorithm shows an accuracy score of 0.82 in simulation deployment and 0.88 in experimentation. In the simulation we used both ducted-centralized and ductless-split cooling systems and in the experimentation we evaluated the solution with ductless-split cooling systems. The overall accuracy of the three-stage technique is 0.82 and 0.86 in simulation and experimentation, respectively. We observe energy savings of up to 68% in simulation and 42% during experimentation, with a reduction of ten days in the cooling system’s downtime and up to 75% in repair cost.

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy.

Investigating interplay between urbanization and carbon emissions is crucial for reaching carbon peak objective. This study employs a VAR model to examine correlation between the urbanization rate and carbon emissions specifically within Guizhou Province, VAR model has obvious advantages in studying the dynamic relationship between them. The findings indicate that: (1) In Guizhou Province, there is a nuanced interplay between the urbanization rate and carbon emissions, with the magnitude and direction of their influence varying across different time intervals. (2) Carbon emissions in Guizhou Province exhibit a notable self-propelling effect, while concurrently, the urbanization rate demonstrates an inertia effect, which also contributes to its own advancement. (3) The influence of the urbanization rate on carbon emissions in Guizhou Province experiences gradual rise before plateauing, suggesting that the high-quality advancement of new urbanization in the region facilitates the achievement of carbon reduction objectives. Finally, policy recommendations are put forward: (1) Conscientiously implement the central ecological environment zoning control policies, such as: Guizhou Province Ecological environment zoning control Plan and Guizhou Province Urban and Rural Construction Carbon peak Implementation Plan and other policies. (2) Pay attention to the quality of Guizhou’s urbanization process. Solve the relationship between urbanization and carbon emissions, and realize the coordination and unification of urbanization and the carrying capacity of resources and environment. (3) Develop a new type of urbanization rich in Guizhou’s mountainous characteristics and promote the construction of low-carbon cities. Give full play to the regional characteristics of Guizhou’s mountainous areas, build a new type of urbanization with Guizhou’s mountainous characteristics, promote the construction of low-carbon cities in the process of urbanization development, and strengthen the coordinated development of ecological environment construction and urbanization.

For a long time, the low-voltage distribution network has the problems of untimely management and complex and frequently changing lines, which makes the problem of missing grid topology information increasingly serious. This study proposes an automatic grid topology detection model based on lasso algorithm and t-distributed random neighbor embedding algorithm. The model identifies the household-variable relationship through the lasso algorithm, and then identifies the grid topology of the station area through the t-distributed random neighbor embedding algorithm model. The experimental results indicated that the lasso algorithm, the constant least squares algorithm and the ridge regression algorithm had accuracies of 0.88, 0.80, and 0.71 and loss function values of 0.14, 0.20, and 0.25 for dataset sizes up to 500. Comparing the time spent on identifying household changes in different regions, in Region 1, the training time for the Lasso algorithm, the Constant Least Squares algorithm, and the Ridge Regression algorithm is 2.8 s, 3.0 s, and 3.1 s, respectively. The training time in region 2 is 2.4s, 3.6s, and 3.4s, respectively. The training time in region 3 is 7.7 s, 1.9 s, and 2.8 s, respectively. The training time in region 4 is 3.1 s, 3.6 s, and 3.3 s, respectively. The findings demonstrate that the suggested algorithmic model performs better than the other and can identify the structure of LV distribution networks.

Building energy consumption in China accounts for 45% of the total national energy consumption, with air conditioning energy consumption representing approximately two-thirds of that. Therefore, energy efficiency in buildings is of utmost importance. This study focuses on a chemical industrial park located along the Fujiang River and compares three heating and cooling supply schemes: the river water source heat pump system, which utilizes river water as the heat source and heat sink; the water cooling unit and boiler system, which uses water-cooled electric compression chillers for cooling and an oil-fired boiler system for heating; and the split air conditioning and gas water heater scheme, which relies on refrigerants such as fluorine-containing compounds for cooling and a gas water heater for heating. By calculating the energy consumption of the above three schemes and conducting a comparative analysis, it is found that the river water source heat pump system exhibits significantly higher energy efficiency throughout the year compared to the water cooling unit and boiler system and the split air conditioning and gas water heater scheme. This highlights the notable energy efficiency advantage of the river water source heat pump system.