25 Sep 2023
 | 25 Sep 2023

Carbon Monitor Power - Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power demand

Léna Gurriaran, Yannig Goude, Katsumasa Tanaka, Biqing Zhu, Zhu Deng, Xuanren Song, and Philippe Ciais

Abstract. The impact of climate change on power demand has become increasingly significant, with changes in temperature, relative humidity, and other climate variables affecting cooling and heating demand for households and industries. Accurately predicting power demand is crucial for energy system planning and management. It is also crucial to understand the evolution of power demand to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and adapt to the impacts of climate change. Artificial intelligence techniques have been used to investigate energy demand-side responses to external factors at various scales in recent years. However, few have explored the impact of climate and weather variability on power demand. This study proposes a data-driven approach to model daily power demand provided by the Carbon Monitor Power project by combining climate variables and human activity indices as predictive features. Our investigation spans the years 2020 to 2022 and focuses on eight countries or groups of countries selected to represent different climates and economies, accounting for over 70 % of global power consumption. These countries include Australia, Brazil, China, the European Union (EU), India, Russia, South Africa, and the United States. We assessed various machine-learning regressors to simulate daily power demand at the national scale. For countries within the EU, we extended the analysis to one group of countries. We evaluated the models based on key evaluating metrics: coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Median Absolute Error (MedAE). We also used the models to identify the most influential variables that impact power demand and apprehend their relationship with it. Our findings provide insight into variations in important predictive features among countries, along with the role played by distinct climate variables and indicators of the level of economic activity, such as weekends and working days, vacations and holidays, and the influence of COVID-19.

Léna Gurriaran et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1313', Matteo De Felice, 23 Oct 2023
  • RC2: 'Comment on egusphere-2023-1313', Giacomo Falchetta, 31 Oct 2023

Léna Gurriaran et al.

Léna Gurriaran et al.


Total article views: 305 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
204 86 15 305 17 5 10
  • HTML: 204
  • PDF: 86
  • XML: 15
  • Total: 305
  • Supplement: 17
  • BibTeX: 5
  • EndNote: 10
Views and downloads (calculated since 25 Sep 2023)
Cumulative views and downloads (calculated since 25 Sep 2023)

Viewed (geographical distribution)

Total article views: 298 (including HTML, PDF, and XML) Thereof 298 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 04 Dec 2023
Short summary
We have developed a data-driven model simulating daily regional power demand based on climate and socioeconomic variables. Our model was applied to eight countries/regions (Australia, Brazil, China, EU, India, Russia, South Africa, US), identifying influential factors and their relationship with power demand. Our findings highlight the significance of economic indicators in addition to temperature, showcasing country-specific variations. This research aids energy planning and emission reduction.