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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 helps plan power investment and production and facilitates 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 to 2017, this study examined the response of residential electricity consumption to temperature. The results show that there is a positive effect of the heating degree day (HDD) and cooling degree day (CDD) on residential electricity consumption. In particular, under the possible influence of the electricity price and weather factor, the electricity-temperature response curve has a “V”-shape when the average temperature is over 30 °C, and an extra day above 34 °C will increase monthly residential electricity consumption by 2.70%. The heterogeneity analysis shows that the temperature and electricity response curve have strong fluctuations under the time-of-use (TOU) pricing policy change. This implies that the price policy helps regulate the power consumption temperature response curve and thus impacts the power load.

When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.

Existing studies have shown that climate change has important implications for residential electricity consumption, yet how responses to climate vary between rural and urban residents, and more importantly, the roles of electricity pricing regimes in determining such responses remain largely unknown. In this paper, we explore these issues using monthly data in Anhui province in China. Our results suggest that on average rural residents are more sensitive to cooling degree days (CDD) than urban counterparts (0.19% vs 0.08% increase in electricity consumption per unit increase in CDD). Additionally, households who adopt the time of use (TOU) pricing regime tend to be less responsive to temperatures than households choosing tiered pricing regimes (TPHE). Substantial increases in electricity demand induced by climate change are expected in the future. With the pessimistic RCP8.5 scenario, our results suggest an increase of 35.5% and 77.1% in electricity demand respectively for the urban and rural residents in the 2080s relative to 2017.

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

A high-speed urban expansion in China over the past two decades has been accompanied by a great leap forward for energy consumption. However, such a significant socio-economic transition may increase the potential risk of energy inequality, which deserves special attention. Using China’s provincial panel data covering the periods of 1997–2020, this paper mainly studies the impact of urbanization on urban-rural electricity consumption inequality with a modified STRIPAT model. The results of the Generalized Method of Moments (GMM) estimation show that there is a significant U-shaped relationship between urbanization and urban-rural electricity consumption inequality. The estimated short-run turning point arrives at the urbanization level of around 63.54% and 61.18% for the long-run estimates. We further carry out a regional heterogeneity analysis and then have two interesting findings: firstly, the colder northern region’s turning point (70.95%) arrives later than the south (57.69%). Secondly, the baseline U-shaped relationship remains for developed eastern regions and the estimated turning point is 57.91%, while for the undeveloped midwestern regions, the relationship is not nonlinear but linearly negative. As an extension, we lastly explore the mechanism underlying the U-shaped relationship, and find that the interaction of urbanization’s scale and efficiency effect determines the U-shaped relationship. Our findings remind policymakers that, to narrow the urban-rural development gap, the future preference of energy policy should be dynamically adaptive to varied regions and development stages.

To cope with the variability of electricity consumption patterns, this study proposes an online rolling optimization-based energy efficiency management strategy for smart homes, which considers user preferences on energy saving and electricity comfort. A weight parameter is used to balance these two objectives and users can set them according to their own electricity preferences. The strategy employs predictive models based on historical data and future inputs to forecast system outputs, and applies feedback correction to compensate prediction errors. In order to better express the power consumption satisfaction of users, two measures of user satisfaction are introduced: utility comfort and temperature comfort. Finally, the simulation result shows that the proposed method achieves an average energy reduction rate of 13.97%, which demonstrates that our strategy can achieve significant reductions in power consumption while enhancing user satisfaction.

Understanding how populations’ daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity-consumption data to unravel behavioral changes within peoples’ homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents—who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates—a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic.

Accurate electricity data is the foundation for time-of-use pricing. However, for various reasons, some data may be incorrect or lost. To address this issue, this paper proposes a recovery algorithm based on differential Fourier transform to restore missing metering data. First, the total electricity consumption data is differentiated and up-sampled as the interpolation sequence. Next, a Fourier transform is performed on the interpolated sequence to convert it from the time domain to the frequency domain. Zero-padding is applied in the high-frequency regions to enhance time-domain resolution. Then, the sequence is converted back to the time domain through an inverse Fourier transform, yielding the missing power consumption sequence. Finally, a proportional scaling method is applied to satisfy the non-decreasing characteristic. Numerical experiments demonstrate that the method proposed in this paper exhibits high reliability and accuracy in restoring missing electricity data.

Droughts and floods pose significant threats to rain-fed agriculture in North China. Ensuring adequate electricity supply during disasters can safeguard food production security and promote sustainable agricultural development. This study proposed a conceptual framework for assessing the water-energy-food nexus from a disaster perspective. Taking 58 cities in North China as a case study, we initially analyzed the correlation among water balance conditions, rural electricity consumption, and food output using a linear approach, which serves to assess the suitability of Copula modeling. Subsequently, the three variables were modeled using five multidimensional Copula functions to deeply explore the intricate correlation structure among them. Finally, using the Copula model and conditional probability analysis, we determined the minimum rural electricity consumption necessary to secure food production security during droughts and floods. The results show that to ensure food production security during drought years, an annual minimum rural electricity consumption of 2100 kWh/person was required in Shandong, 1800 kWh/person in Henan, 2100 kWh/person in Hebei, 900 kWh/person in Shanxi, 1950 kWh/person in Beijing, and 1950 kWh/person in Tianjin. Similarly, to ensure food production security during flood years, an annual minimum of 2700 kWh/person of rural electricity consumption was guaranteed in Shandong, 2100 kWh/person in Henan, 2850 kWh/person in Hebei, 2100 kWh/person in Shanxi, 2250 kWh/person in Beijing, and 3000 kWh/person in Tianjin. The findings of the study can provide decision support for food security work in North China.

Addressing the impacts of climate change has become a global public crisis and challenge. China is characterized by a complex and diverse topography and vast territory, which makes it worthwhile to explore the differential impacts of climate change on urban electricity consumption in different zones and economic development conditions. This study examines the differential impact of climate factors on urban electricity consumption in China based on monthly panel data for 282 prefectures from 2011 to 2019 and projects the potential demand for future urban electricity consumption under different climate change scenarios. The results show that (1) temperature changes significantly alter urban electricity consumption, with cooling degree days (CDD) and heating degree days (HDD) contributing positively to urban electricity consumption in areas with different regional and economic development statuses, with elasticity coefficients of 0.1015–0.1525 and 0.0029–0.0077, respectively. (2) The temperature-electricity relationship curve shows an irregular U-shape. Each additional day of extreme weather above 30 °C and below −12 °C increases urban electricity consumption by 0.52% and 1.52% in the north and by 2.67% and 1.32% in the south. Poor cities are significantly more sensitive to extremely low temperatures than rich cities. (3) Suppose the impacts of climate degradation on urban electricity consumption are not halted. In that case, the possible Shared Socioeconomic Pathways 1-1.9 (SSP1-1.9), SSP1-2.6, and SSP2-4.5 will increase China’s urban electricity consumption by 1621.96 billion kWh, 2960.87 billion kWh, and 6145.65 billion kWh, respectively, by 2090. Finally, this study makes some policy recommendations and expectations for follow-up studies.