杨大智科研成果

Info Research Publications On forecasting Teaching 新建主栏目 基本信息 名称 Dazhi Yang（杨大智），1985年生，博士，教授，博士生导师。分别于2009，2012，2015年在新加坡国立大学获得学士，硕士，博士学位。2020年获得国家高层次人才计划（青年）项目支持。2017年成为领域权威期刊《Solar Energy》最年轻的编委，2019年起出任该杂志的区域主编。国际能源署“高渗透和大规模太阳能资源应用”分区的亚洲代表。共发表SCI论文140余篇，总引用7300（谷歌学术），H-因子46，i-10因子112。太阳能预报的SCI文章数量世界第一。以第一作者身份与加州大学Jan Kleissl教授共同撰写世界第一部太阳能预报的专著。2020、2021、2022年连续入选斯坦福大学全球前2%顶尖科学家榜单（包括终身科学影响力排行榜以及年度科学影响力排行榜）。2021、2022、2023年连续入选由全球学者库发表的全球顶尖前10万科学家榜单。 Dazhi Yang is a professor with the Harbin Institute of Technology. He received the B.Eng., M.Sc., and Ph.D. degrees from the Department of Electrical Engineering, National University of Singapore, Singapore, in 2009, 2012, and 2015, respectively. In 2020, he received support from the National Talent Program, which is a high-prestige research award by the Ministry of Industry and Information Technology of China. In 2017, he became the youngest associate editor of the Solar Energy journal, and has been serving as one of four subject editors of that journal since 2019. He is an active participant in the International Energy Agency, Photovoltaic Power Systems Programme, Task 16. He has published more than 140 journal papers, with a total citation number of 7300 (Google Scholar), an H-index of 46, and an i-10 index of 112. Currently, he has most journal publications on solar forecasting in the world. In 2020, 2021, and 2022, he is listed as one of the world's top 2% scientists (for both single year and career) by Stanford University. In 2021, 2022, and 2023, he is listed as one of the world's top 100,000 scientists published by the Global Scholars Database. 工作经历 标题 起讫时间 2021.03-present 职位/职称 Professor (教授) 工作单位 Harbin Institute of Technology (哈尔滨工业大学) 简单介绍 标题 起讫时间 2015.02-2021.02 职位/职称 Scientist (研究员) 工作单位 Agency for Science, Technology and Research (新加坡科技研究局) 简单介绍 标题 起讫时间 2012.02-2015.01 职位/职称 Research Engineer (工程师) 工作单位 Solar Energy Research Institute of Singapore (新加坡太阳能实验室) 简单介绍 教育经历 标题 Doctor of Philosophy 起讫时间 2012.08-2015.02 所学专业 Electrical Engineering 学习机构 National University of Singapore (新加坡国立大学) 学历 Ph.D. 简单介绍 标题 Master of Science 起讫时间 2010.08-2012.07 所学专业 Electrical Engineering 学习机构 National University of Singapore (新加坡国立大学) 学历 M.Sc. 简单介绍 标题 Bachelor of Engineering 起讫时间 2005.08-2009.07 所学专业 Electrical Engineering 学习机构 National University of Singapore (新加坡国立大学) 学历 B.Eng. 简单介绍 主要任职 任职名称 Subject Editor, Solar Energy 任职时间 2019.01-present 简单介绍 任职名称 Associate Editor, Solar Energy 任职时间 2017.05-2018.12 简单介绍 Research area 名称 Energy forecasting（能源预报） Solar forecasting（太阳能预报） Physical model chain（物理模型链） Predictability（可预测性分析） Load forecasting（负荷预报） Wind forecasting（风能预报） Solar resource assessment（太阳能资源评估） Radiation modeling（太阳辐射建模） Satellite-to-irradiance（天基辐射遥感） Ground-based radiometry（地基辐射测量） Atmospheric science（大气科学） Retrieval（反演） NWP（天气预报模式） Reanalysis（再分析数据） Atmospheric radiation （大气辐射） Aerosol（气溶胶） Power system（电力系统） Microgrid configuration（微网配置） Day-ahead dispatching（日前调度） Hydrogen energy system（氢能系统） Large-scale grid integration（大规模并网） Hierarchical modeling（分层式建模） Firm generation（稳固发电） Battery model predictive control（储能模型预测控制） Battery fault diagnosis（电池故障检测） Spatio-temporal statistics（空间统计） Kriging（克里格法） Covariance function（协方差函数） Data fusion（数据融合） Data analytics（数据分析） R（R语言） Python（Python语言） Team 名称 2023 Team: 前排从左至右：财务通，领导能力突出，比我聪明的没我漂亮，猫咪之友，南理四号种子，开始发力的赠一，影模型和后演 后排从左至右：迟到不早退，南理一号种子，踮脚大王，大管家，杨站长，情谊佰，工大押宝对象，与哈工大双向奔赴的少年，两栖明星王志文，不爱吃花生的闪电，安全帽换电脑 2022 Team: 前排从左至右：幼儿园园长，山东好女婿，火箭探伤和采蘑菇都是一样的，二阶改锥师傅，未来国网总工，《想说爱你不容易》，A类生源靠前，欲拿长发换杰青 后排从左至右：不会且不来实验室的迷之少年，触电数学家，校级青年人才，不毕业不理发，不去西苑吃午饭，大牛比较懒，乒乓球手下败将，买一赠一，年轻不拿国奖 2021 Team: 前排从左至右：潇总，大师姐，钱老板，乒乓顾，严肃魏 后排从左至右：爱斯基摩佳，大老师，数学宇，国网项目技术骨干，昊哥，“这个报不了” Book title Solar Radiation Modeling in Energy Meteorology author Hongrong Shi, Xiang'ao Xia, Dazhi Yang publication time 2026 press CRC Press introduction title Solar Irradiance and Photovoltaic Power Forecasting author Dazhi Yang, Jan Kleissl publication time 2024 press CRC Press introduction Book Chapter title Solar Project Financing, Bankability, and Resource Assessment author Dazhi Yang, Licheng Liu publication time 2020 press Springer International Publishing introduction Journal 名称 Corresponding author: §; Journal IF of a particular year follows the JCR release for the same year 2024 (29) Zhang, X., Yang, D. §, Zhang, H., Liu, B., Li, M., Chu, Y., Wang, J., Xia, X., 2024. 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Short–term solar irradiance forecasting using exponential smoothing state space model. Energy 55, 1104–1113. https://doi.org/10.1016/j.energy.2013.04.027 (IF:4.159) 2012 (1) 1. Yang, D. §, Jirutitijaroen, P., Walsh, W.M., 2012. Hourly solar irradiance time series forecasting using cloud cover index. Solar Energy 86(12), 3531–3543. https://doi.org/10.1016/j.solener.2012.07.029 (IF:2.952) Conference 名称 Too many to list. Bertrand Russell's message to the future generation 名称 My view on solar forecasting 名称 A forecast is a prediction of a future event or quantity; forecasts can thus be considered as a subset of predictions. Forecasting has three principal characteristics: first and foremost, it only consists in dealing with events or quantities that are partially predictable, in that, it is concerned with neither those events or quantities which can be known with absolute certainty, nor those to which there is nothing, other than issuing a random guess, can be done; the second source of theory is that a forecast must carry a notion of impact, such that its value can be materialized during decision-making; and the third attraction of pragmatism is that forecasts can only be verified at a future time, and so they are liable to the danger of circular reasoning, making the concocted explanations unfalsifiable. These characteristics are briefly considered in the following three paragraphs. People often think they know more than they actually do—this knowledge gap is called epistemic arrogance. Consequently, people who know little about forecasting are tempted to think in the extremes: either that forecasts are not worthy of pursuing since there is no certainty in them, or that science is mature enough for all kinds of forecasting. Arguments employed by people in the first type of extreme often come from a perspective that is guided by memorable disappointments from the past. Upon repeated conditioning by stories on how forecasts have failed—"The weather man is wrong again,'' "economists fail to forecast the recession,'' "the sports analyst's predicted score is far off the actual one''—one may lose faith in activities of the similar kind and become eventually a skeptic. What the skeptics fail to notice, however, is how far weather forecasting has come in the past century, how complex and unpredictable economics is, and how the actual score was affected by an early injury of a star player. Skeptics tend to look for instances that do not meet their expectations (or confirm their theories that forecasting does not work), yet ignore those that do. This could in turn lead to what is more generally known as a confirmation bias. Equally hazardous as scepticism on forecasting is to place a blind trust on it, which describes the second type of extreme. The reason for this danger is much simpler—if stock market forecast accuracy was exact, we would all be millionaires. In both extremes of forecasting sceptics and worshipers, what needs to be factored in is predictability—the degree to which a good forecast can be made. "Good,'' as has been debated by philosophers for millenniums, is an abstract notion. The goodness of a forecast, with no exception, is subject to interpretation. To assign any meaning to it, one must put it in context. One may interpret a forecast to be good if it corresponds to the forecaster's best judgment. How to issue a forecast truthfully and how to detect a untruthful forecast have been the topic under heated debate among forecasters. In other circumstances, one may interpret a forecast to be good if it performs better than its alternatives under some specific scoring rules. This is known as forecast verification, in which forecasts generated by different forecasters are compared with respect to the matching observations. Having established these interpretations, there is still something missing, that is, a forecast by itself possesses no intrinsic value, but can only acquire value through its ability to influence the decision made by its user. It is rational to believe a truthful forecast with high performance is more likely to result in higher value, the relationship among truthfulness, quality, and value is, however, almost surely nonlinear and may not even be monotonic. In any case, it leads to the conclusion that forecasting is only performed if it can be appreciated by the potential user of the forecasts. The last characteristic of forecasting is tied to the philosophy of science, insofar as all scientific methods require induction from specific facts to general laws. Human beings are hardwired to learn specifics when in fact they should be concerned with generalities. As argued by Bertrand Russell, who is perhaps the greatest philosopher of the twentieth century and the greatest logician since Aristotle, all inductive arguments in the last resort reduce to the form: "if this is true, that is true; now that is true, therefore this is true.'' This is a bare display of a circular reasoning fallacy. However, this seemingly foolish form of argument would not be fundamentally different from those upon which all scientific laws are based. In forecasting, we often argue that since some specific facts obey certain law, therefore other facts of a similar sort should obey the same law. In other words, we fit data to derive a forecasting model, and expect that model can be used to explain the future. We may verify the law in a region of the future by waiting for that to happen, but the practical importance of forecasting is always in regard to those regions of the future where the law has not yet been verified. Hence, the doubt as to the validity of induction is an inherent limitation of not just forecasting, but scientific methods in general. Be that as it may, because this limitation affects the whole of scientific knowledge, one has no choice but to assume pragmatically that the inductive procedure, with appropriate caveats, is admissible. 名称 Ph.D. modules Academic writing: Be like Russell M.Eng. modules Energy forecasting