Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energy supply. A promising approach to anticipate irradiance changes consists of modeling the cloud cover dynamics from ground taken or satellite images. This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables. We evaluate the models on a set of irradiance measurements and corresponding sky images collected in Palaiseau (France) over 8 months with a temporal resolution of 2 min. To outline the learning of neural networks in the context of short-term irradiance forecasting, we implemented visualisation techniques revealing the types of patterns recognised by trained algorithms in sky images. In addition, we show that training models with past samples of the same day improves their forecast skill, relative to the smart persistence model based on the Mean Square Error, by around 10% on a 10 min ahead prediction. These results emphasise the benefit of integrating previous same-day data in short-term forecasting. This, in turn, can be achieved through model fine tuning or using recurrent units to facilitate the extraction of relevant temporal features from past data.
Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards
Stanford University（斯坦福大学）；Open Climate Fix（开放气候修复）；National Center for Atmospheric Research（美国国家大气研究中心）
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance …
Universidad Nacional de Colombia（哥伦比亚国立大学）；University of Florida（佛罗里达大学）
Accurate mechanisms for forecasting solar irradiance and insolation provide important information for the planning of renewable energy and agriculture projects as well as for environmental and socio-economical studies. This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation that only requires solar irradiance historical data for training. Furthermore, our approach is able to deal with missing data since it includes a data imputation state. In the prediction stage, we consider four data-driven approaches: Autoregressive Integrated Moving Average …
Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Université du Québec à Rimouski（魁北克大学里穆斯基分校）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）
An Explainable, Attention-Enhanced, Bidirectional Long Short-Term Memory Neural Network for Joint 48-Hour Forecasting of Temperature, Irradiance, and Relative Humidity
Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校计算工程与科学研究所）；Energy Institute, The University of Texas at Austin（德克萨斯大学奥斯汀分校能源研究所）
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）
Delft University of Technology（代尔夫特理工大学）；VITO（弗拉芒技术研究院）
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements …
Due to the limitation of data availability, traditional power load forecasting methods focus more on studying the load variation pattern and the influence of only a few factors such as temperature and holidays, which fail to reveal the inner mechanism of load variation. This paper breaks the limitation and collects 80 potential features from astronomy, geography, and society to study the complex nexus between power load variation and influence factors, based on which a short-term power load forecasting method is proposed. Case studies show that, compared with the state-of-the-art methods, the …
The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This investigation aims to decrease the additional cost by introducing probabilistic multi-task intra-hour solar forecasting (feasible in real time applications) to increase the penetration of photovoltaic systems in power grids. The direction of moving clouds is estimated in consecutive sequences of sky images by extracting features of cloud dynamics with the objective of forecasting the global solar irradiance that reaches photovoltaic …
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this s…
University of La Reunion（留尼汪大学）；CNRS（法国国家科学研究中心）；University of Corsica（科西嘉大学）
This paper proposes to use a rather new modelling approach in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving Average (ARMA) and Neural Network (NN) models are combined to form a model committee. The Bayesian inference is used to affect a probability to each model in the committee. Hence, each model's predictions are weighted by their respective probability. The models are fitted to one year of hourly Global Horizontal Irradiance (GHI) measurements. Another year (the test set) is used for making genuine one hour ahead (h+1) out-of-sample fo…
Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
Advanced Concepts Team European Space Agency（欧洲航天局高级概念团队）；Department of Physics Università degli Studi di Roma Sapienza（罗马大学物理系）；Department of Engineering Science University of Oxford（牛津大学工程科学系）；Department of Information and Computer Science University of Hawai’i at Mānoa（夏威夷大学信息与计算机科学系）；Free Flight Research Lab（自由飞行研究实验室）；NASA Headquarters（美国国家航空航天局总部）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of New Hampshire（新罕布什尔大学）；Department of Computer Science University of Oxford, UK（牛津大学计算机科学系）；Department of Computer Science University of York & King’s College London（约克大学计算机科学系及伦敦国王学院计算机科学系）
Swami Vivekananda Institute of Science & Technology（斯瓦米·维韦卡南达科学技术学院）；Department of Computer Science and Engineering（计算机科学与工程系）
Photovoltaic power is a renewable source of energy which is highly used in industries. In economically struggling countries it can be a potential source of electric energy as other non-renewable resources are already exhausting. Now if installation of a photovoltaic cell in a region is done prior to research, it may not provide the desired energy output required for running that region. Hence forecasting is required which can elicit the output from a particular region considering its geometrical coordinates, solar parameter like GHI and weather parameters like temperature and wind speed etc. O…
ADAPT SFI Research Centre（ADAPT SFI研究中心）；University College Dublin（都柏林大学学院）；Advanced Digital Sciences Center (ADSC), University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校高级数字科学中心（ADSC））；Nanyang Technological University (NTU)（南洋理工大学（NTU））；National University of Singapore (NUS)（新加坡国立大学（NUS））
Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and …
With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistical distinctions between different hours, and hourly similarities in solar data to improve the forecasting accuracy. The HS-based method is built by training multiple two-layer multi-model forecasting framework (MMFF) models independently with the same-hour subsets. The final opti…
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）
National University of Singapore（新加坡国立大学）；Institute of High Performance Computing（高性能计算研究院）
Numerical weather prediction (NWP) and machine learning (ML) methods are popular for solar forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models with different possible parameterizations. In this study, an alternative approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then po…
University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（昂吉集团CRIGEN实验室）
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels. However, current approaches to modelling the cloud cover dynamics from sky images still lack precision regarding the spatial configuration of clouds, their temporal dynamics and physical interactions with solar radiation. Benefiting from a growing number of large datasets, data driven methods are being developed to address these limitations with prom…
Carnegie Mellon University（卡内基梅隆大学）；IBM Research Labs（IBM研究院实验室）；GE Power（通用电气电力公司）
Ahead-of-time forecasting of incident solar-irradiance on a panel is indicative of expected energy yield and is essential for efficient grid distribution and planning. Traditionally, these forecasts are based on meteorological physics models whose parameters are tuned by coarse-grained radiometric tiles sensed from geo-satellites. This research presents a novel application of deep neural network approach to observe and estimate short-term weather effects from videos. Specifically, we use time-lapsed videos (sky-videos) obtained from upward facing wide-lensed cameras (sky-cameras) to directly e…
The University of Texas at Dallas（得克萨斯大学达拉斯分校）；National Renewable Energy Laboratory (NREL)（国家可再生能源实验室）；IBM TJ Watson Research Center（IBM托马斯·J·沃森研究中心）
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting. This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The dai…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques re…
International University of Rabat（拉巴特国际大学）；University of Leeds（利兹大学）
We present a novel approach to perform ground-based estimation and prediction of the surface solar irradiance with the view to predicting photovoltaic energy production. We propose the use of mini-batch k-means clustering to extract features, referred to as per cluster number of pixels (PCNP), from sky images taken by a low-cost fish eye camera. These features are first used to classify the sky as clear or cloudy using a single hidden layer neural network; the classification accuracy achieves 99.7%. If the sky is classified as cloudy, we propose to use a deep neural network having as input fea…
Recent advances in Computer Vision and Deep Learning have enabled astonishing results in a variety of fields and applications. Motivated by this success, the SkyCam Dataset aims to enable image-based Deep Learning solutions for short-term, precise prediction of solar radiation on a local level. For the span of a year, three different cameras in three topographically different locations in Switzerland are acquiring images of the sky every 10 seconds. Thirteen high resolution images with different exposure times are captured and used to create an additional HDR image. The images are paired with …
University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（ENGIE CRIGEN实验室）；MINES ParisTech（巴黎高等矿业学院）
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional architectures require specific rotational invariant layers or extensive data augmentation to learn from diverse rotated versions of a given spatial configuration. Unwrapping the image into its polar coordinates provides a more explicit representation to train a convolutional architecture as the rotational invariance becomes translational, hence the visua…
Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting
Ecole Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院）；Mechanical Engineering Department, Universidad de Chile（智利大学机械工程系）；Departamento de Ingeniería Mecánica y Metalúrgica, Escuela de Ingeniería, Pontificia Universidad Católica de Chile（智利天主教大学机械工程与冶金系）；Garrick Institute for the Risk Sciences, University of California（加州大学风险科学研究所）
Advanced Digital Sciences Center, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校高级数字科学中心）；Nanyang Technological University（南洋理工大学）
Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation. In this paper, we show how images taken by WSIs can be used to estimate solar radiation. Sky cameras are useful here because they provide additional information about cloud movement and coverage, which are otherwise not available from weather station data. Our setup includes ground-based weather stations at the same location as the imagers. We use their measurements to validate our methods.
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）
Nanyang Technological University (NTU)（南洋理工大学）；Advanced Digital Sciences Center (ADSC)（高级数字科学中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The analysis of clouds in the earth's atmosphere is important for a variety of applications, viz. weather reporting, climate forecasting, and solar energy generation. In this paper, we focus our attention on the impact of cloud on the total solar irradiance reaching the earth's surface. We use weather station to record the total solar irradiance. Moreover, we employ collocated ground-based sky camera to automatically compute the instantaneous cloud coverage. We analyze the relationship between measured solar irradiance and computed cloud coverage value, and conclude that higher cloud coverage …
Institute of Networked and Embedded Systems（网络化与嵌入式系统研究所）；Lakeside Labs（湖畔实验室）
Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted and reliable power supply to serve the energy demand. There are several approaches to perform solar irradiance forecasting, for instance satellite-based methods, sky image-based methods, machine learning-based methods, and numerical weather prediction-based methods. In this pape…
Stanford University（斯坦福大学）；Energy and Environmental Economics, Inc.（能源与环境经济公司）
Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardized benchmark datasets for image-based solar forecasting, which limits the comparison of different forecasting models and the exploration of forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains…
Nanyang Technological University（南洋理工大学）；Trinity College Dublin（都柏林大学三一学院）；Agency for Science, Technology and Research（科学、技术和研究局）
The total amount of solar irradiance falling on the earth's surface is an important area of study amongst the photo-voltaic (PV) engineers and remote sensing analysts. The received solar irradiance impacts the total amount of generated solar energy. However, this generation is often hindered by the high degree of solar irradiance variability. In this paper, we study the main factors behind such variability with the assistance of Global Positioning System (GPS) and ground-based, high-resolution sky cameras. This analysis will also be helpful for understanding cloud phenomenon and other events i…
University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（昂吉集团CRIGEN实验室）
Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine…
Nanyang Technological University（南洋理工大学）；Advanced Digital Sciences Center（高级数字科学中心）
The estimation of total solar irradiance falling on the earth's surface is important in the field of solar energy generation and forecasting. Several clear-sky solar radiation models have been developed over the last few decades. Most of these models are based on empirical distribution of various geographical parameters; while a few models consider various atmospheric effects in the solar energy estimation. In this paper, we perform a comparative analysis of several popular clear-sky models, in the tropical region of Singapore. This is important in countries like Singapore, where we are primar…
