Exploring the climatic impacts on residential electricity consumption in Jiangsu, China

Zhang, Mingyang; Zhang, Kaiwen; Hu, Wuyang; Zhu, Bangzhu; Wang, Ping; Wei, Yi‐Ming

doi:10.1016/j.enpol.2020.111398

Cited by 50 publications

(16 citation statements)

References 37 publications

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“…Therefore, electricity consumption is highly correlated to the temperature. In the residential sector, the fair electricity consumers have a fairly good correlation with temperature [42]. As shown in the Figure 6 (red bars), the temperature-electricity consumption correlation grades of fair electricity consumers are much higher than suspicious electricity consumers.…”

Section: ) Step 1 Of Labeling: Intra-consumer Electricity Consumption Analysismentioning

confidence: 88%

Electricity Theft Detection With Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm

et al. 2021

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Electricity theft has emerged as one of the major reasons of Non-Technical Losses (NTLs) in the power distribution systems and has become a global issue. Therefore, power utilities are concerned about resolving the issue of electricity theft. In this regard, the data collected by Advanced Metering Infrastructure (AMI) can be used to devise data-driven machine learning-based Electricity Theft Detection (ETD) methods. In this paper, a novel data-driven ETD method is proposed that firstly labels the electricity consumers as fair or malicious based on three analyses: intra-consumer, inter-consumer, and temperature-electricity consumption relation. After assigning labels to the data, significant features are extraction from data by introducing a new feature extractor that is based on Reconstruction Independent Component Analysis (RICA) and sparse auto-encoder. Finally, classification is performed using two newly proposed enhanced classifiers, named as Differential Evolution (DE) Random Undersampling Boosting (DE-RUSBoost) and Jaya-RUSBoost. The performance of RUSBoost is enhanced using two nature-inspired swarm intelligence-based optimization algorithms, namely DE and Jaya optimization. The performance evaluation of the proposed classifiers is performed by conducting comprehensive simulations on real-world data taken from the UMass * smart homes electricity consumption dataset and the State Grid Corporation of China (SGCC) electricity theft dataset. DE-RUSBoost achieves an Area Under the Curve (AUC) of 0.89 and Jaya-RUSBoost achieves AUC of 0.95. The proposed classifiers have superior performance compared to two state-of-the-art benchmarks, i.e., Wide And Deep Convolution Neural Network (WADCNN) and grid search-based RUSBoost.

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Section: ) Step 1 Of Labeling: Intra-consumer Electricity Consumption Analysismentioning

confidence: 88%

Electricity Theft Detection With Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm

et al. 2021

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“…This suggests that the southeastern part of the basin is more prone to serious concurrent extremes than other parts. The southeastern part of the HRB belongs to Jiangsu Province, one of the strongest economic regions of China and a major population centre (Zhang et al ., 2020), where concurrent extremes markedly impact local economies and human health.…”

Section: Discussionmentioning

confidence: 99%

Substantial decrease in concurrent meteorological droughts and consecutive cold events in Huai River Basin, China

Zhang

Yang

Chen

2021

Intl Journal of Climatology

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Concurrent meteorological droughts and consecutive cold events can significantly impact local ecosystems, socio‐economies, and human health. The respective characteristics of droughts and cold extremes have been extensively studied, however, their concurrent extremes have received very little attention. In this study, we used the Huai River Basin as a case to build a magnitude index of concurrent meteorological droughts and consecutive cold events (CDCMI) based on daily minimum temperature data and a 1‐month standardized precipitation evapotranspiration index (SPEI) from 1961 to 2018. Results show that the magnitude of meteorological droughts increased over the observation period, while consecutive cold events significantly decreased. CDCMI showed a significant downward trend at a rate of −0.14 per decade. Large differences were observed between 1990–2018 and 1961–1989—especially in the southern parts of the basin (around −50%). The frequency of mild and moderate concurrent meteorological droughts and consecutive cold events showed no significant upward or downward trends, but severe and extreme concurrent events showed pronounced decreasing trends at rates of −0.03 events/decade and − 0.036 events/decade, respectively. Under the same return period, high CDCMI values are present in the southeastern part of the basin, indicating that the concurrent events in this region are more serious. When CDCMI = 1.79 (extreme grade), the return period in the southeastern part of the basin (5–10 years) is much lower than that in the northwestern part of the basin (>100 years), indicating that this part is harmed to a greater extent by the concurrent extremes than elsewhere in the basin. The spatial pattern of maximum CDCMI again indicates that the southeastern part of the basin is at a high risk for concurrent events. The proposed magnitude index may be a useful tool for analysing concurrent (compound) droughts and cold events as well as their potential impacts.

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“…For instance, Li (2018) measured the temperature with the monthly average air temperature and built a multivariate linear model based on the data in respect of residential electricity usage and climate in Singapore. Zhang et al (2020) collected the panel data of Jiangsu province and evaluated the fluctuations of urban and rural residential electricity usage under HDD and CDD. It was found that the high heating demand in summer and cooling demand in winter led to an increase in electricity usage.…”

Section: Literature Reviewmentioning

confidence: 99%

Temperature impacts on residential electricity consumption in Anhui, China: Does the electricity price matter?

Song

et al. 2022

Preprint

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Global warming leads to the problem of climate adaptability, which makes residents' electricity consumption behavior more sensitive to temperature. Understanding the shape of the temperature–electricity consumption response curve is helpful for planning power investment and production, and to facilitate a green and low-carbon transformation of the power system. Using data regarding electricity consumption in nearly 20,000 households from seven cities in Anhui Province, China, from 2016–2017, this study examined the response of residential electricity consumption to temperature. The results show that although residential electricity consumption was proportional to the average temperature, the variation range of the residential electricity consumption was not the same in each percentile range of temperature. In particular, under the possible influence of the electricity price and weather factor, the residential electricity consumption temperature response curve has a "V"-shape when the average temperature is over 30°C. The heterogeneity analysis shows that the temperature and electricity response curve have strong fluctuations under the change of the time-of-use (TOU) pricing policy. This implies that the price policy helps to regulate the power consumption temperature response curve and thus has an impact on the power load.

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Exploring the climatic impacts on residential electricity consumption in Jiangsu, China

Cited by 50 publications

References 37 publications

Electricity Theft Detection With Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm

Electricity Theft Detection With Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm

Substantial decrease in concurrent meteorological droughts and consecutive cold events in Huai River Basin, China

Temperature impacts on residential electricity consumption in Anhui, China: Does the electricity price matter?

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