A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning

India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India’s electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India’s coal power generation as the country transitions towards its renewable energy targets.印度计划于2070年实现净零排放，并设定了到2030年达成500吉瓦（GW）可再生发电装机容量的宏伟目标。2022年，燃煤电厂发电量占印度总发电量的60%以上。对高排放燃煤电厂进行升级改造与脱碳处理，已成为当前亟待解决的能源议题。燃煤电厂的关键技术参数为运行机组热耗率（Station Heat Rate, SHR），其代表燃煤电厂的热效率水平。然而印度燃煤电厂的实际运行热耗率存在显著差异，且相关数据尚未得到全面收录与记录。本研究依托多个现有数据库，借助机器学习（Machine Learning, ML）方法，构建了覆盖806台印度燃煤机组的热耗率数据集，是目前覆盖范围最全面的同类数据集。为提升预测精度，本研究还纳入水资源胁迫风险、煤炭价格等环境与经济要素作为预测特征。该数据集可通过本研究的可视化平台便捷下载，可为印度在向可再生能源转型以达成其既定能源目标的过程中，燃煤发电领域的能源与环境政策制定提供科学参考依据。