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Measurement(s) carbon dioxide emissions Technology Type(s) fossil fuel consumption

Abstract Non‐intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed‐NILM is proposed. In Fed‐NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed‐NILM. In addition, a comparison of Fed‐NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed‐NILM exhibits superior performance in terms of scalability and convergence. Fed‐NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed‐NILM significantly improves the co‐modelling capabilities of local data owners while protecting the privacy of power consumers.

Abstract Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises’ production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.

With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.