Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.
CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting
School of Electrical and Computer Engineering（电气与计算机工程学院）；Cornell University（康奈尔大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Northeastern University（东北大学）；Fu Foundation School of Engineering and Applied Science（富兰克林基金会工程与应用科学学院）；Columbia University in the City of New York（纽约市哥伦比亚大学）；School of Engineering（工程学院）；Santa Clara University（圣克拉拉大学）；Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）；University of Houston（休斯顿大学）
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
National University of Sciences and Technology（国家科学技术大学）；Ghulam Ishaq Khan Institute of Engineering Sciences and Technology（古拉姆·伊沙克·汗工程科学与技术学院）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Uppsala University（乌普萨拉大学）
Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting
College of Engineering, Cornell University（康奈尔大学工程学院）；Department of Electrical and Computer Engineering, University of California, San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电气工程系）；Dyson School of Design Engineering, Imperial College London（伦敦帝国理工学院设计工程学院）
CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting
NSF Artificial Intelligence Institute for Advances in Optimization（NSF人工智能优化研究所）；H. Milton Stewart School of Industrial and Systems Engineering（H. Milton Stewart工业与系统工程学院）；Georgia Institute of Technology（佐治亚理工学院）
Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation
Guangzhou Power Supply Bureau Guangdong Power Grid Co., Ltd.（广东电网有限责任公司广州供电局）；Tsinghua University（清华大学）；Tsinghua-Berkeley Shenzhen Institute (TBSI)（清华-伯克利深圳学院）
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Lucas English 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）；Mahdi Abolghasemi School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Mahdi Abolghasemi 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）
Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting
organization= School of Science, Engineering \& Technology, East Delta University , addressline = Abdullah Al Noman Road, Noman Society , city= Chattagram , postcode= 4209 , country= Bangladesh
Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning
School of Automation Science and Engineering, Xi’an Jiaotong University（西安交通大学自动化科学与工程学院）；School of Electronic Information Engineering, Shanghai Dianji University（上海电机学院电子信息工程学院）；School of Energy and Power Engineering, Xi'an Jiaotong University（西安交通大学能源与动力工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；School of Microelectronics, Xi’an Jiaotong University（西安交通大学微电子学院）；State Key Laboratory for Manufacturing System Engineering Lab, Xi’an Jiaotong University（西安交通大学制造系统工程国家重点实验室）
R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Center for Urban Science and Computation, Tsinghua University（城市科学与计算中心，清华大学）；Courant Institute of Mathematical Sciences, New York University（数学科学学院，纽约大学）
Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants
i-EM S.r.l（i-EM有限公司）；Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa（比萨大学能源、系统、领土和建筑工程系）；Enel Green Power S.p.A.（意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；Renewable Energy Management, Enel Green Power S.p.A.（可再生能源管理，意大利国家电力公司绿色电力分公司（Enel Green Power S.p.A.））；University of Pisa（比萨大学）
Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD)（智能工程与高级创新发展计算研究组（GECAD））；Porto School of Engineering (ISEP)（波尔图工程学院（ISEP））
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy
ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting
School of Data Science, Chinese University of Hong Kong - Shen Zhen（数据科学学院，香港中文大学（深圳））；School of Management and Engineering, Nanjing University（管理与工程学院，南京大学）；Department of Information Engineering and Computer Science, Feng Chia University（信息工程与计算机科学系，逢甲大学）；Graduate School of Information, Production and Systems, Waseda University（信息、生产与系统研究生学校，早稻田大学）
