AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University（香港理工大学建筑环境与能源工程系）；International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University（香港理工大学城市能源 nexus 中心）；Research Institute for Smart Energy, The Hong Kong Polytechnic University（香港理工大学智能能源研究院）；Eastern Institute for Advanced Study, Eastern Institute of Technology（东院高级研究机构）；School of Business, Society and Technology, Mälardalens University（马尔默大学商学院、社会与技术学院）；Center for Spatial Information Science, the University of Tokyo（东京大学空间信息科学中心）；PV Industry Innovation Center, State Power Investment Corporation（国家电力投资集团光伏产业创新中心）；School of Urban Planning and Design, Peking University, Shenzhen（北京大学深圳校区城市规划与设计学院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究所）
Photovoltaic power forecasting (PVPF) is a critical area in time series forecasting (TSF), enabling the efficient utilization of solar energy. With advancements in machine learning and deep learning, various models have been applied to PVPF tasks. However, constructing an optimal predictive architecture for specific PVPF tasks remains challenging, as it requires cross-domain knowledge and significant labor costs. To address this challenge, we introduce AutoPV, a novel framework for the automated search and construction of PVPF models based on neural architecture search (NAS) technology. We develop a brand new NAS search space that incorporates various data processing techniques from state-of-the-art (SOTA) TSF models and typical PVPF deep learning models. The effectiveness of AutoPV is evaluated on diverse PVPF tasks using a dataset from the Daqing Photovoltaic Station in China. Experimental results demonstrate that AutoPV can complete the predictive architecture construction process in a relatively short time, and the newly constructed architecture is superior to SOTA predefined models. This work bridges the gap in applying NAS to TSF problems, assisting non-experts and industries in automatically designing effective PVPF models.
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）
Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究院，东方理工大学）；Peng Cheng Laboratory（鹏城实验室）；National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology（深圳应用数学中心，南方科技大学）
Characterization and forecasting of national-scale solar power ramp events
School of Engineering and Computer Science, Bern University of Applied Sciences（伯尔尼应用科学大学工程与计算机科学学院）；Department of Geosciences and Remote Sensing, TU Delft（代尔夫特理工大学地球科学与遥感系）
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Faculty of Electrical Engineering, Czestochowa University of Technology（捷斯托霍瓦技术大学电气工程学院）；Faculty of Mathematics and Computer Science, University of Łódź（卢布林大学数学与计算机科学学院）；CAMINO - Centre for Data Analysis, Modelling and Computational Sciences, University of Łódź（卢布林大学CAMINO数据与建模计算科学中心）
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）
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
Department of Mechanical Engineering, School of Electrical, Mechanical and Infrastructure Engineering, The University of Melbourne, Melbourne, Australia（机械工程系，电气、机械与基础设施工程学院，墨尔本大学，墨尔本，澳大利亚）；Department of Mechanical Engineering, Sri Lanka Institute of Information Technology, Sri Lanka（机械工程系，斯里兰卡信息科技研究所，斯里兰卡）；School of Computing and Information Systems, Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia（计算与信息学院，墨尔本数据科学中心，墨尔本大学，墨尔本，澳大利亚）
Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models
School of Engineering and Computer Science, Bern University of Applied Sciences（工程与计算机科学学院，伯恩应用科学大学）；Department of Geosciences and Remote Sensing, TU Delft（地球科学与遥感系，代尔夫特理工大学）
SolarSeer: Ultrafast and accurate 24-hour solar irradiance forecasts outperforming numerical weather prediction across the USA
Microsoft（微软公司）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University of California, Berkeley（加州大学伯克利分校）；Taiyuan University of Technology（太原科技大学）
Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Computer Science and Technology, BNRist, Tsinghua University（清华大学计算机科学与技术系）；Beijing Smartchip Microelectronics Technology Co., Ltd（北京智芯微电子技术有限公司）
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation
SunPower Corporation（SunPower公司）；SolarEdge Technologies（SolarEdge科技公司）；Case Western Reserve University（凯斯西储大学）；C2 Energy Capital（C2能源资本公司）；Sandia National Labs（桑迪亚国家实验室）
MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
Department of Naval, Electrical, Electronics；Telecommunications Engineering, University of Genoa, Via all’Opera Pia 11a, 16145 Genoa, Italy；Unit of Innovation, Entrepreneurship \& Sustainability, Department of Engineering, University Campus Bio-Medico of Rome Via Alvaro del Portillo 21, 00128 Rome, Italy；Computer Systems Department of Engineering, University Campus Bio-Medico of Rome Via Alvaro del Portillo 21, 00128 Rome, Italy
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
