Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting

Kong, Weicong; Jia, Youwei; Zhang, Dong; Meng, Ke; Chai, Songjian

doi:10.1016/j.apenergy.2020.115875

Cited by 71 publications

(26 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extracted features, as well as other meteorological data are then analyzed by an MLP to forecast 10-min DNI, achieving a forecast skill of 17.06%. Kong et al (Kong et al, 2020) investigates both static sky image units and dynamic sky image stream and proposes a hybrid sky image-based forecasting model that outperforms a reference model without image inputs by 32.8% during ramp events. To tackle the issue of imbalanced sky images data set, resampling and data augmentation methods have been proposed for end-to-end hybrid models (Nie et al, 2021).…”

Section: Ll Open Accessmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

View full text Add to dashboard Cite

The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

show abstract

Section: Ll Open Accessmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

View full text Add to dashboard Cite

show abstract

“…In very short-term forecasting of solar irradiance the cloud information in the images is useful to accurately predict when the Sun may be occluded by a cloud. In these events, a shadow is projected over a PV system producing a drop in the energy supply [6] , [7] . A GSI forecasting algorithm will provide the grid with the capability of managing the energy resources [8] .…”

Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

View full text Add to dashboard Cite

The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

show abstract

“…The proposed irradiance nowcasting module is composed of multimodal LSTM networks Hochreiter and Schmidhuber (1997), irradiance readings LSTM (irLSTM) and sky image LSTM (siLSTM). Although LSTM has been used in solar forecasting Zhang et al (2018), Kong et al (2020), Zheng et al (2020), the multimodal LSTM approach and the learning architecture that is proposed in this paper are novel contributions. Conv-LSTM is a variant of the LSTM network which is specifically designed to learn the spatial information of data.…”

Section: Irradiance Nowcasting Modulementioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

View full text Add to dashboard Cite

The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

show abstract

Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting

Cited by 71 publications

References 22 publications

Intra-hour irradiance forecasting techniques for solar power integration: A review

Intra-hour irradiance forecasting techniques for solar power integration: A review

Girasol, a sky imaging and global solar irradiance dataset

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

Contact Info

Product

Resources

About