2411-3336 2541-9404 Записки Горного института Journal of Mining Institute Санкт-Петербургский горный университет императрицы Екатерины ΙΙ Empress Catherine II Saint Petersburg Mining University DEFRIP pmi-16345 https://pmi.spmi.ru/pmi/article/view/16345 Энергетика Energy industry Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations Повышение интерпретируемости моделей прогнозирования электропотребления горно-добывающих предприятий с помощью аддитивного объяснения Шепли Matrenin Pavel V. Матренин П. В. Matrenin Pavel V. matrenin.2012@corp.nstu.ru 0000-0001-5704-0976 Уральский федеральный университет имени первого Президента России Б.Н.Ельцина (Екатеринбург, Россия) Ural Federal University named after the first President of Russia B.N.Yeltsin (Yekaterinburg, Russia) Stepanova Alina I. Степанова А. И. Stepanova Alina I. a.i.stepanova@urfu.ru 0000-0002-3484-2295 Уральский федеральный университет имени первого Президента России Б.Н.Ельцина (Екатеринбург, Россия) Ural Federal University named after the first President of Russia B.N.Yeltsin (Yekaterinburg, Russia) 16 10 2024 2025 271 154 167 10 11 2023 03 06 2024 25 02 2025 © 2024 П. В. Матренин, А. И. Степанова © 2024 Pavel V. Matrenin, Alina I. Stepanova 2024 П. В. Матренин, А. И. Степанова Pavel V. Matrenin, Alina I. Stepanova Эта статья доступна по лицензии Creative Commons Attribution 4.0 International (CC BY 4.0) This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) https://pmi.spmi.ru/pmi/article/view/16345

Цель исследования – повышение уровня доверия пользователей систем прогнозирования графиков нагрузки предприятий путем применения методов объяснимого искусственного интеллекта, которые позволяют получить не только прогноз, но и его обоснование. Объект исследования – комплекс шахт и горно-обогатительных комбинатов предприятия, покупающего электроэнергию на оптовом рынке электроэнергии и мощности. Использованы почасовые данные электропотребления за два года, график плановых ремонтов и остановов оборудования, метеорологические данные. Применены ансамбли деревьев решений для прогнозирования временных рядов, выполнен анализ влияния различных факторов на точность прогнозирования. Предложен алгоритм интерпретации результатов прогноза с помощью метода аддитивного объяснения Шепли. Средняя по модулю относительная ошибка прогнозирования составила 7,84 % с учетом метеорологических факторов, 7,41 % с учетом метеорологических факторов и плана нагрузки, сформированного экспертом. Ошибка прогноза эксперта составляла 9,85 %. Полученные результаты показывают, что повышенная с учетом дополнительных факторов точность прогноза электропотребления повышается еще больше при совмещении методов машинного обучения и экспертной оценки. Создание такой системы возможно только при применении моделей объяснимого искусственного интеллекта.

The objective of this study is to enhance user trust in electricity consumption forecasting systems for mining enterprises by applying explainable artificial intelligence methods that provide not only forecasts but also their justifications. The research object comprises a complex of mines and ore processing plants of a company purchasing electricity on the wholesale electricity and power market. Hourly electricity consumption data for two years, schedules of planned repairs and equipment shutdowns, and meteorological data were utilized. Ensemble decision trees were applied for time series forecasting, and an analysis of the impact of various factors on forecasting accuracy was conducted. An algorithm for interpreting forecast results using the SHapley Additive exPlanation method was proposed. The mean absolute percentage error was 7.84 % with consideration of meteorological factors, 7.41 % with consideration of meteorological factors and a load plan formulated by an expert, and the expert's forecast error was 9.85 %. The results indicate that the increased accuracy of electricity consumption forecasting, considering additional factors, further improves when combining machine learning methods with expert evaluation. The development of such a system is only feasible using explainable artificial intelligence models.

 Ключевые слова прогнозирование электропотребления горно-обогатительные предприятия оптовый рынок электроэнергии и мощности объяснимый искусственный интеллект ансамблевые модели вектор Шепли Keywords electricity consumption forecasting mining enterprises wholesale electricity and power market explainable artificial intelligence ensemble models Shapley vector Исследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации в рамках Программы развития Уральского федерального университета имени первого Президента России Б.Н.Ельцина в соответствии с программой стратегического академического лидерства «Приоритет-2030». The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University named after the First President of Russia B.N.Yeltsin Program of Development within the Priority-2030 Program) is gratefully acknowledged. Park S., Ruy S., Choi Y. et al. Data-Driven Baseline Estimation of Residential Buildings for Demand Response // Energies. 2015. Vol. 8. Iss. 9. P. 10239-10259. DOI: 10.3390/en80910239 Park S., Ruy S., Choi Y. et al. Data-Driven Baseline Estimation of Residential Buildings for Demand Response. Energies. 2015. Vol. 8. Iss. 9, p. 10239-10259. DOI: 10.3390/en80910239 Almuhaini S.H., Sultana N. Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management // Energies. 2023. Vol. 16. Iss. 4. № 2035. DOI: 10.3390/en16042035 Almuhaini S.H., Sultana N. Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management. Energies. 2023. Vol. 16. Iss. 4. N 2035. DOI: 10.3390/en16042035 Faria P., Vale Z. Demand Response in Smart Grids // Energies. 2023. Vol. 16. Iss. 2. № 863. DOI: 10.3390/en16020863 Faria P., Vale Z. Demand Response in Smart Grids. Energies. 2023. Vol. 16. Iss. 2. N 863. DOI: 10.3390/en16020863 Канапелько Р.А. Российская и зарубежная практика взаимодействия корпоративных и властных структур оптового рынка электроэнергии и мощности // Экономика и бизнес: теория и практика. 2019. № 2. С. 47-51. DOI: 10.24411/2411-0450-2019-10364 Kanapelko R.A. Russian and foreign practice of interaction between corporate and government structures of the wholesale electricity and capacity market. Journal of Economy and Business. 2019. N 2, p. 47-51 (in Russian). DOI: 10.24411/2411-0450-2019-10364 Matrenin P.V., Manusov V.Z., Khalyasmaa A.I. et al. Improving Accuracy and Generalization Performance of Small-Size Re-current Neural Networks Applied to Short-Term Load Forecasting // Mathematics. 2020. Vol. 8. Iss. 12. № 2169. DOI: 10.3390/math8122169 Matrenin P.V., Manusov V.Z., Khalyasmaa A.I. et al. Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting. Mathematics. 2020. Vol. 8. Iss. 12. N 2169. DOI: 10.3390/math8122169 Антоненков Д.В., Матренин П.В. Исследование ансамблевых и нейросетевых методов машинного обучения в задаче краткосрочного прогнозирования электропотребления горных предприятий // Электротехнические системы и комплексы. 2021. № 3 (52). С. 57-65. DOI: 10.18503/2311-8318-2021-3(52)-57-65 Antonenkov D.V., Matrenin P.V. Ensemble and Neural Network Machine Learning Models for Short-Term Load Forecasting of Open Cast Mining Companies. Electrotechnical Systems and Complexes. 2021. N 3 (52), p. 57-65 (in Russian). DOI: 10.18503/2311-8318-2021-3(52)-57-65 Непша Ф.С., Красильников М.И., Перевалов К.В. Применение цифровой платформы для построения интеллектуаль-ных систем управления энергоснабжением предприятий горной промышленности // Автоматизация и IT в энергетике. 2021. № 5 (142). C. 26-34. Nepsha F.S., Krasilnikov M.I., Perevalov K.V. Application of a digital platform to build intelligent energy management systems for mining enterprises. Avtomatizatsiya i IT v energetike. 2021. N 5 (142), p. 26-34. (in Russian) Jianjun Fan, Xinzhong Liu, Zhimin Li et al. Power load forecasting research based on neural network and Holt-winters method // IOP Conference Series: Earth and Environmental Science. 2021. Vol. 692. № 022120. DOI: 10.1088/1755-1315/692/2/022120 Jianjun Fan, Xinzhong Liu, Zhimin Li et al. Power load forecasting research based on neural network and Holt-winters method. IOP Conference Series: Earth and Environmental Science. 2021. Vol. 692. N 022120. DOI: 10.1088/1755-1315/692/2/022120 Potapov V., Khamitov R., Makarov V. et al. Short-Term Forecast of Electricity Load for LLC «Omsk Energy Retail Company» Using Neural Network / 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics), 13-15 November 2018, Omsk, Russia. IEEE, 2018. DOI: 10.1109/Dynamics.2018.8601430 Potapov V., Khamitov R., Makarov V. et al. Short-Term Forecast of Electricity Load for LLC “Omsk Energy Retail Company” Using Neural Network. 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics), 13-15 November 2018, Omsk, Russia. IEEE, 2018. DOI: 10.1109/Dynamics.2018.8601430 Chodakowska E., Nazarko J., Nazarko Ł. ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise // Energies. 2021. Vol. 14. Iss. 23. № 7952. DOI: 10.3390/en14237952 Chodakowska E., Nazarko J., Nazarko Ł. ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise. Energies. 2021. Vol. 14. Iss. 23. N 7952. DOI: 10.3390/en14237952 Sharma S., Majumdar A., Elvira V., Chouzenoux É. Blind Kalman Filtering for Short-Term Load Forecasting // IEEE Transactions on Power Systems. 2020. Vol. 35. Iss. 6. P. 4916-4919. DOI: 10.1109/TPWRS.2020.3018623 Sharma S., Majumdar A., Elvira V., Chouzenoux É. Blind Kalman Filtering for Short-Term Load Forecasting. IEEE Transactions on Power Systems. 2020. Vol. 35. Iss. 6, p. 4916-4919. DOI: 10.1109/TPWRS.2020.3018623 Madhukumar M., Sebastian A., Xiaodong Liang et al. Regression Model-Based Short-Term Load Forecasting for University Campus Load // IEEE Access. 2022. Vol. 10. P. 8891-8905. DOI: 10.1109/ACCESS.2022.3144206 Madhukumar M., Sebastian A., Xiaodong Liang et al. Regression Model-Based Short-Term Load Forecasting for University Campus Load. IEEE Access. 2022. Vol. 10, p. 8891-8905. DOI: 10.1109/ACCESS.2022.3144206 Серебряков Н.А. Применение ансамбля глубоких нейронных сетей в задачах краткосрочного прогнозирования по-часового электропотребления гарантирующего поставщика электроэнергии // Электротехнические системы и комплексы. 2021. № 2 (51). С. 52-60. DOI: 10.18503/2311-8318-2021-2(51)-52-60 Serebryakov N.A. Application of Deep Neural Network Ensemble in a Problem of Short-Term Load Forecasting Guaranteed Electricity Supplier. Electrotechnical Systems and Complexes. 2021. N 2 (51), p. 52-60 (in Russian). DOI: 10.18503/2311-8318-2021-2(51)-52-60 Caro E., Juan J., Nouhitehrani S. Optimal Selection of Weather Stations for Electric Load Forecasting // IEEE Access. 2023. Vol. 11. P. 42981-42990. DOI: 10.1109/ACCESS.2023.3270933 Caro E., Juan J., Nouhitehrani S. Optimal Selection of Weather Stations for Electric Load Forecasting. IEEE Access. 2023. Vol. 11, p. 42981-42990. DOI: 10.1109/ACCESS.2023.3270933 Sergeev N., Matrenin P. Improving Accuracy of Machine Learning Based Short-Term Load Forecasting Models with Cor-relation Analysis and Feature Engineering / 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM), 29 June – 3 July 2023, Novosibirsk, Russia. IEEE, 2023. P. 1000-1004. DOI: 10.1109/EDM58354.2023.10225058 Sergeev N., Matrenin P. Improving Accuracy of Machine Learning Based Short-Term Load Forecasting Models with Correlation Analysis and Feature Engineering. 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM), 29 June – 3 July 2023, Novosibirsk, Russia. IEEE, 2023, p. 1000-1004. DOI: 10.1109/EDM58354.2023.10225058 Habbak H., Mahmoud M., Metwally K. et al. Load Forecasting Techniques and Their Applications in Smart Grids // Energies. 2023. Vol. 16. Iss. 3. № 1480. DOI: 10.3390/en16031480 Habbak H., Mahmoud M., Metwally K. et al. Load Forecasting Techniques and Their Applications in Smart Grids. Energies. 2023. Vol. 16. Iss. 3. N 1480. DOI: 10.3390/en16031480 Ryu S., Noh J., Kim H. Deep Neural Network Based Demand Side Short Term Load Forecasting // Energies. 2017. Vol. 10. Iss. 1. № 3. DOI: 10.3390/en10010003 Ryu S., Noh J., Kim H. Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies. 2017. Vol. 10. Iss. 1. N 3. DOI: 10.3390/en10010003 Szczepaniuk H., Szczepaniuk E.K. Applications of Artificial Intelligence Algorithms in the Energy Sector // Energies. 2023. Vol. 16. Iss. 1. № 347. DOI: 10.3390/en16010347 Szczepaniuk H., Szczepaniuk E.K. Applications of Artificial Intelligence Algorithms in the Energy Sector. Energies. 2023. Vol. 16. Iss. 1. N 347. DOI: 10.3390/en16010347 Lizhen Wu, Chun Kong, Xiaohong Hao, Wei Chen. A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model // Mathematical Problems in Engineering. 2020. Vol. 2020. Iss. 1. № 1428104. DOI: 10.1155/2020/1428104 Lizhen Wu, Chun Kong, Xiaohong Hao, Wei Chen. A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model. Mathematical Problems in Engineering. 2020. Vol. 2020. Iss. 1. N 1428104. DOI: 10.1155/2020/1428104 Zhuofu Deng, Binbin Wang, Yanlu Xu et al. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting // IEEE Access. 2019. Vol. 7. P. 88058-88071. DOI: 10.1109/ACCESS.2019.2926137 Zhuofu Deng, Binbin Wang, Yanlu Xu et al. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access. 2019. Vol. 7, p. 88058-88071. DOI: 10.1109/ACCESS.2019.2926137 Weicong Kong, Zhao Yang Dong, Youwei Jia et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network // IEEE Transactions on Smart Grid. 2019. Vol. 10. Iss. 1. P. 841-851. DOI: 10.1109/TSG.2017.2753802 Weicong Kong, Zhao Yang Dong, Youwei Jia et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid. 2019. Vol. 10. Iss. 1, p. 841-851. DOI: 10.1109/TSG.2017.2753802 Потапов В.И., Грицай А.С., Тюньков Д.А., Синицин Г.Э. Использование нейронной сети для построения кратко-срочного прогноза электропотребления ООО «Омская энергосбытовая компания» // Известия Томского политехнического университета. Инжиниринг георесурсов. 2016. Т. 327. № 8. С. 44-51. Potapov V.I., Gritsay A.S., Tyunkov D.A., Sinitsin G.E. Using neural network for building short-term forecast of electricity load of LLC “Omsk Energy Retail Company”. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering. 2016. Vol. 327. N 8, p. 44-51 (in Russian). Cai M., Pipattanasomporn M., Rahman S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques // Applied Energy. 2019. Vol. 236. P. 1078-1088. DOI: 10.1016/j.apenergy.2018.12.042 Cai M., Pipattanasomporn M., Rahman S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Applied Energy. 2019. Vol. 236, p. 1078-1088. DOI: 10.1016/j.apenergy.2018.12.042 Ye Hong, Yingjie Zhou, Qibin Li et al. A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid // IEEE Access. 2020. Vol. 8. P. 55785-55797. DOI: 10.1109/ACCESS.2020.2981817 Ye Hong, Yingjie Zhou, Qibin Li et al. A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid. IEEE Access. 2020. Vol. 8, p. 55785-55797. DOI: 10.1109/ACCESS.2020.2981817 Zhuofu Deng, Binbin Wang, Yanlu Xu et al. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting // IEEE Access. 2019. Vol. 7. P. 88058-88071. DOI: 10.1109/ACCESS.2019.2926137 Zhuofu Deng, Binbin Wang, Yanlu Xu et al. Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access. 2019. Vol. 7, p. 88058-88071. DOI: 10.1109/ACCESS.2019.2926137 Ahmad N., Ghadi Y., Adnan M., Ali M. Load Forecasting Techniques for Power System: Research Challenges and Survey // IEEE Access. 2022. Vol. 10. P. 71054-71090. DOI: 10.1109/ACCESS.2022.3187839 Ahmad N., Ghadi Y., Adnan M., Ali M. Load Forecasting Techniques for Power System: Research Challenges and Survey. IEEE Access. 2022. Vol. 10, p. 71054-71090. DOI: 10.1109/ACCESS.2022.3187839 Клюев Р.В., Моргоева А.Д., Гаврина О.А. и др. Прогнозирование планового потребления электроэнергии для объе-диненной энергосистемы с помощью машинного обучения // Записки Горного института. 2023. Т. 261. С. 392-402. Klyuev R.V., Morgoeva A.D., Gavrina O.A. et al. Forecasting planned electricity consumption for the united power system using machine learning. Journal of Mining Institute. 2023. Vol. 261, p. 392-402. Rusina A.G., Filippova T.A., Kalinin A.E., Terlyga N.S. Short-Term Electricity Consumption Forecast in Siberia IPS Using Climate Aspects / 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), 29 June – 3 July 2018, Erlagol, Russia. IEEE, 2018. P. 6403-6407. DOI: 10.1109/EDM.2018.8435002 Rusina A.G., Filippova T.A., Kalinin A.E., Terlyga N.S. Short-Term Electricity Consumption Forecast in Siberia IPS Using Climate Aspects. 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM), 29 June – 3 July 2018, Erlagol, Russia. IEEE, 2018, p. 6403-6407. DOI: 10.1109/EDM.2018.8435002 Моргоев И.Д., Дзгоев А.Э., Клюев Р.В., Моргоева А.Д. Прогнозирование потребления электроэнергии предприятиями народнохозяйственного комплекса в условиях неполноты информации // Известия Кабардино-Балкарского научного центра РАН. 2022. № 3 (107). С. 9-20. DOI: 10.35330/1991-6639-2022-3-107-9-20 Morgoev I.D., Dzgoev A.E., Klyuev R.V., Morgoeva A.D. Forecasting of electricity consumption by enterprises of the public sector complex in conditions of incomplete information. Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAS. 2022. N 3 (107), p. 9-20 (in Russian). DOI: 10.35330/1991-6639-2022-3-107-9-20 Antonenkov D.V., Solovev D.B. Mathematic simulation of mining company’s power demand forecast (by example of «Neryungri» coal strip mine) // IOP Conference Series: Earth and Environmental Science. 2017. Vol. 87. Iss. 3. № 032003. DOI: 10.1088/1755-1315/87/3/032003 Antonenkov D.V., Solovev D.B. Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine). IOP Conference Series: Earth and Environmental Science. 2017. Vol. 87. Iss. 3. N 032003. DOI: 10.1088/1755-1315/87/3/032003 Моргоева А.Д., Моргоев И.Д., Клюев Р.В., Гаврина О.А. Прогнозирование потребления электрической энергии про-мышленным предприятием с помощью методов машинного обучения // Известия Томского политехнического университета. Инжиниринг георесурсов. 2022. Т. 333. № 7. С. 115-125. DOI: 10.18799/24131830/2022/7/3527 Morgoeva A.D., Morgoev I.D., Klyuev R.V., Gavrina O.A. Forecasting of electric energy consumption by an industrial enterprise using machine learning methods. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering. 2022. Vol. 333. N 7, p. 115-125 (in Russian). DOI: 10.18799/24131830/2022/7/3527 Вялкова С.А., Моргоева А.Д., Гаврина О.А. Разработка гибридной модели прогнозирования потребления электриче-ской энергии для горно-металлургического предприятия // Устойчивое развитие горных территорий. 2022. Т. 14. № 3 (53). С. 486-493. DOI: 10.21177/1998-4502-2022-14-3-486-493 Vyalkova S.A., Morgoeva A.D., Gavrina O.A. Development of a hybrid model for predicting the consumption of electrical energy for a mining and metallurgical enterprise. Sustainable Development of Mountain Territories. 2022. Vol. 14. N 3 (53), p. 486-493 (in Russian). DOI: 10.21177/1998-4502-2022-14-3-486-493 Khalyasmaa A., Matrenin P. Initial Data Corruption Impact on Machine Learning Models’ Performance in Energy Con-sumption Forecast / 2021 Ural-Siberian Smart Energy Conference (USSEC), 13-15 November 2021, Novosibirsk, Russian Federation. IEEE, 2021. 5 p. DOI: 10.1109/USSEC53120.2021.9655724 Khalyasmaa A., Matrenin P. Initial Data Corruption Impact on Machine Learning Models’ Performance in Energy Consumption Forecast. 2021 Ural-Siberian Smart Energy Conference (USSEC), 13-15 November 2021, Novosibirsk, Russian Federation. IEEE, 2021, p. 5. DOI: 10.1109/USSEC53120.2021.9655724 Bouktif S., Fiaz A., Ouni A., Serhani M.A. Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches // Energies. 2018. Vol. 11. Iss. 7. № 1636. DOI: 10.3390/en11071636 Bouktif S., Fiaz A., Ouni A., Serhani M.A. Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies. 2018. Vol. 11. Iss. 7. N 1636. DOI: 10.3390/en11071636 Манусов В.З. Нейронные сети: прогнозирование электрической нагрузки и потерь мощности в электрических сетях. От романтики к прагматике. Новосибирск: Изд-во НГТУ, 2018. 303 с. Manusov V.Z. Neural networks: forecasting of electrical loads and power losses in electric grids. From romanticism to pragmatism. Novosibirsk: NSTU Publisher, 2018, p. 303 (in Russian). Tianqi Chen, Guestrin C. XGBoost: A Scalable Tree Boosting System / KDD’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17 August 2016, San Francisco, CA, USA. New York: As-sociation for Computing Machinery, 2016. P. 785-794. DOI: 10.1145/2939672.2939785 Tianqi Chen, Guestrin C. XGBoost: A Scalable Tree Boosting System. KDD’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17 August 2016, San Francisco, CA, USA. New York: Association for Computing Machinery, 2016, p. 785-794. DOI: 10.1145/2939672.2939785 Ahmed I., Jeon G., Piccialli F. From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where // IEEE Transactions on Industrial Informatics. 2022. Vol. 18. Iss. 8. P. 5031-5042. DOI: 10.1109/TII.2022.3146552 Ahmed I., Jeon G., Piccialli F. From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where. IEEE Transactions on Industrial Informatics. 2022. Vol. 18. Iss. 8, p. 5031-5042. DOI: 10.1109/TII.2022.3146552 Adadi A., Berrada M. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) // IEEE Access. 2018. Vol. 6. P. 52138-52160. DOI: 10.1109/ACCESS.2018.2870052 Adadi A., Berrada M. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access. 2018. Vol. 6, p. 52138-52160. DOI: 10.1109/ACCESS.2018.2870052 Ribeiro M.T., Singh S., Guestrin C. «Why Should I Trust You?»: Explaining the Predictions of Any Classifier / The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Pro-ceedings of the Demonstrations Session, 12-17 June 2016, San Diego, CA, USA. Stroudsburg: Association for Computational Lin-guistics, 2016. P. 97-101. DOI: 10.48550/arXiv.1602.04938 Ribeiro M.T., Singh S., Guestrin C. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Proceedings of the Demonstrations Session, 12-17 June 2016, San Diego, CA, USA. Stroudsburg: Association for Computational Linguistics, 2016, p. 97-101. DOI: 10.48550/arXiv.1602.04938 Selvaraju R.R., Cogswell M., Das A. et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Locali-zation // International Journal of Computer Vision. 2020. Vol. 128. Iss. 2. P. 336-359. DOI: 10.1007/s11263-019-01228-7 Selvaraju R.R., Cogswell M., Das A. et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision. 2020. Vol. 128. Iss. 2, p. 336-359. DOI: 10.1007/s11263-019-01228-7 Lundberg S.M., Su-In Lee. A unified approach to interpreting model predictions / NIPS’17: Proceedings of the 31st Interna-tional Conference on Neural Information Processing Systems, 4-9 December 2017, Long Beach, CA, USA. Red Hook: Curran Asso-ciates Inc., 2017. P. 4768-4777. DOI: 10.48550/arXiv.1705.07874 Lundberg S.M., Su-In Lee. A unified approach to interpreting model predictions. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 4-9 December 2017, Long Beach, CA, USA. Red Hook: Curran Associates Inc., 2017, p. 4768-4777. DOI: 10.48550/arXiv.1705.07874 Kuzlu M., Cali U., Sharma V., Güler Ö. Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools // IEEE Access. 2020. Vol. 8. P. 187814-187823. DOI: 10.1109/ACCESS.2020.3031477 Kuzlu M., Cali U., Sharma V., Güler Ö. Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools. IEEE Access. 2020. Vol. 8, p. 187814-187823. DOI: 10.1109/ACCESS.2020.3031477 Hengbo Liu, Ziqing Ma, Linxiao Yang et al. SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events / ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, 4-10 June 2023, Rhodes Island, Greece. IEEE, 2023. 5 p. DOI: 10.1109/ICASSP49357.2023.10096002 Hengbo Liu, Ziqing Ma, Linxiao Yang et al. SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events. ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, 4-10 June 2023, Rhodes Island, Greece. IEEE, 2023, p. 5. DOI: 10.1109/ICASSP49357.2023.10096002