Yuhao Nie

Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential. In this study, we explore three different training strategies for solar forecasting models by leveraging three heterogeneous datasets collected globally with different climate patterns. Specifically, we compare the performance of local models trained individually based on single datasets and global models trained jointly based on the fusion of multiple datasets, and further examine the knowledge transfer from pre-trained solar forecasting models to a new dataset of interest. The results suggest that the local models work well when deployed locally, but significant errors are observed when applied offsite. The global model can adapt well to individual locations at the cost of a potential increase in training efforts. Pre-training models on a large and diversified source dataset and transferring to a target dataset generally achieves superior performance over the other two strategies. With 80% less training data, it can achieve comparable performance as the local baseline trained using the entire dataset.

In recent years, deep learning-based solar forecasting using all-sky images has emerged as a promising approach for alleviating uncertainty in PV power generation. However, the stochastic nature of cloud movement remains a major challenge for accurate and reliable solar forecasting. With the recent advances in generative artificial intelligence, the synthesis of visually plausible yet diversified sky videos has potential for aiding in forecasts. In this study, we introduce \emphSkyGPT, a physics-informed stochastic video prediction model that is able to generate multiple possible future images of the sky with diverse cloud motion patterns, by using past sky image sequences as input. Extensive experiments and comparison with benchmark video prediction models demonstrate the effectiveness of the proposed model in capturing cloud dynamics and generating future sky images with high realism and diversity. Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model. Better PV output prediction reliability and sharpness is observed by using the predicted sky images generated with SkyGPT compared with other benchmark models, achieving a continuous ranked probability score (CRPS) of 2.81 (13% better than SUNSET and 23% better than smart persistence) and a Winkler score of 26.70 for the test set. Although an arbitrary number of futures can be generated from a historical sky image sequence, the results suggest that 10 future scenarios is a good choice that balances probabilistic solar forecasting performance and computational cost.

Sky image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty of solar power generation. However, a major challenge is the lack of large quantity of sky image data encompassing diverse sky conditions for model training. This study presents a comprehensive survey of open-source sky image datasets for solar forecasting and related research areas, including cloud segmentation, classification and motion prediction which could potentially enhance solar forecasting capabilities. In total, 72 open-source sky image datasets are identified globally that satisfy the needs of deep learning-based method development. A database containing information about various aspects of the identified datasets is constructed. A multi-criteria ranking system is further developed to evaluate each dataset based on eight dimensions which could have important impacts on the data usage. Finally, insights on the applications of these datasets are provided. This study streamlines the processes of identifying and selecting sky image datasets, and could potentially accelerate the method development and benchmark in solar forecasting and related fields including energy meteorology and atmospheric science.

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 three years (2017–2019) of quality-controlled down-sampled sky images and PV power generation data that is ready-to-use for short-term solar forecasting using deep learning. In addition, to support the flexibility in research, we provide the high resolution, high frequency sky images and PV power generation data as well as the concurrent sky video footage. We also include a code base containing data processing scripts and baseline model implementations for researchers to reproduce our previous work and accelerate their research in solar forecasting.

Photovoltaics (PV), the primary use of solar energy, is growing rapidly. However, the variable output of PV under changing weather conditions may hinder the large-scale deployment of PV. In this study, we propose a two-stage classification-prediction framework to predict contemporaneous PV power output from sky images (a so-called “nowcast”), and compare it with an end-to-end convolution neural network (CNN). The proposed framework first classifies input images into different sky conditions and then the classified images are sent to specific sub-models for PV output prediction. Two types of classifiers are developed and compared: (1) a CNN-based classifier trained on clear sky index (CSI)-labeled sky images and (2) a physics-based non-parametric classifier based on a threshold of fractional cloudiness of sky images. Different numbers of classification categories are also examined. The results suggest that the cloudiness-based classifier is more suitable than the CSI-based classifier for the framework, and the 3-class classification (i.e., sunny, cloudy, overcast) is found to be the optimal choice. We then fine-tune the cloudiness threshold for the non-parametric classifier and tailor the architecture for each sky-condition-specific sub-model. Under the best design, the proposed framework can achieve a root mean squared error (RMSE) of 2.20 kW (relative to a 30 kW rated PV array) on the test set comprising 18 complete days (9 sunny, RMSE = 0.69 kW; 9 cloudy, RMSE = 3.06 kW). Compared with the end-to-end CNN baseline model, the overall prediction performance can be improved by 6% (7% in sunny and 6% in cloudy), with 6% fewer trainable parameters needed in the architecture.

Liquefied natural gas (LNG) is a promising alternative to coal to mitigate the greenhouse gas (GHG) and particulate emissions from power, industry, and district heating in China. While numerous existing life cycle assessment (LCA) studies estimate the GHG footprint of LNG, large variation exists in these results. Such variability could be caused by differing project designs, system boundaries, modeling methods and data sources. It is not clear which of these factors is the most important. Here, three research groups from Canada and the US performed independent LCAs of the same planned LNG supply chain from Canada to China. The teams applied different methods and assumptions but used aligned system boundaries and worked with a single upstream producer to obtain production data. The GHG emissions of Canadian LNG to China for power and heat generation were found to be 427–556 g CO2-eq/kWh and 81–92 g CO2-eq/MJ. Compared with Chinese coal for power generation, 291–687 g CO2-eq (34%–62%) reduction can be achieved per kWh of power generated. The central tendency in each study is aligned more closely than the overall uncertainty range: thus, uncertainty caused by fundamental data challenges likely outweighs variability caused by use of different LCA methods. Differences in assumptions and methods among the three teams lead to moderate variation at the stage level, but in better agreement at the life-cycle level, showing the existence of compensating variation. Given the robustness to very different LCA methods, existing literature variation may be explained by project-, location- and operator-dependent parameters.

Biofuels from hydrothermal liquefaction (HTL) of abundantly available forest residues in British Columbia (BC) can potentially make great contributions to reduce the greenhouse gas (GHG) emissions from the transportation sector. A life-cycle assessment was conducted to quantify the GHG emissions of a hypothetic 100 million liters per year HTL biofuel system in the Coast Region of BC. Three scenarios were defined and investigated, namely, supply of bulky forest residues for conversion in a central integrated refinery (Fr-CIR), HTL of forest residues to bio-oil in distributed biorefineries and subsequent upgrading in a central oil refinery (Bo-DBR), and densification of forest residues in distributed pellet plants and conversion in a central integrated refinery (Wp-CIR). The life-cycle GHG emissions of HTL biofuels is 20.5, 17.0, and 19.5 g CO2-eq/MJ for Fr-CIR, Bo-DBR, and Wp-CIR scenarios, respectively, corresponding to 78–82% reduction compared with petroleum fuels. The conversion stage dominates the total GHG emissions, making up more than 50%. The process emitting most GHGs over the life cycle of HTL biofuels is HTL buffer production. Transportation emission, accounting for 25% of Fr-CIR, can be lowered by 83% if forest residues are converted to bio-oil before transportation. When the credit from biochar applied for soil amendment is considered, a further reduction of 6.8 g CO2-eq/MJ can be achieved. Converting forest residues to bio-oil and wood pellets before transportation can significantly lower the transportation emission and contribute to a considerable reduction of the life-cycle GHG emissions. Process performance parameters (e.g., HTL energy requirement and biofuel yield) and the location specific parameter (e.g., electricity mix) have significant influence on the GHG emissions of HTL biofuels. Besides, the recycling of the HTL buffer needs to be investigated to further improve the environmental performance of HTL biofuels.