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

Abstract

The impact of climate change on power demand and power generation has become increasingly significant. Changes in temperature, relative humidity, and other climate variables affect cooling and heating demand for households and industries and, therefore, power generation. Accurately predicting power generation is crucial for energy system planning and management. It is also crucial to understand the evolution of power generation to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and to 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 determine 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.