Hourly electricity load curve dataset for Chinese provinces derived from meteorological variables.

As the world's largest electricity consumer, China has long faced a shortage of publicly available electricity load data at a high temporal resolution. To address this limitation, this study leverages the strong correlation between electricity demand and meteorological conditions, as well as the broad accessibility of hourly weather data. By combining the limited publicly released load statistics from the National Development and Reform Commission with detailed hourly observations of temperature, wind speed, solar irradiance, and relative humidity, we estimated province-level power coefficients and threshold temperatures for both cooling and heating. These coefficients were then used to reconstruct hourly electricity load profiles for all provinces. The resulting dataset provides hourly electricity demand for 31 provincial-level regions across China for the period 2015-2024. Importantly, the proposed methodology is highly scalable and can be extended to any target year using annual electricity consumption data, air conditioner ownership per household, and corresponding hourly meteorological inputs. This dataset offers a valuable empirical foundation for research on electricity demand dynamics and long-term energy system planning in China.