Accurate solar generation forecasting is critical for ensuring power system reliability, economics, and effectiveness and controlling the supply-demand balance. This research offers novel multi-branch spatio-temporal forecasting models to improve forecasting accuracy and minimize forecasting errors. The first step is to build temporal models employing advanced deep learning architectures, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and GRU with Attention (AttGRU). Next, spatio-temporal solar forecasting models are constructed. A novel multi-branch Attentive Gated Recurrent Residual network (ResAttGRU) consisting of multiple branches of residual networks (ResNet), GRU, and the attention mechanism is introduced. The proposed multi-branch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multi-time-scale representative, while GRUs can exploit temporal information at less computational cost than the popular LSTM. The novelty of the developed architecture is in the utilization of multiple convolutional-based branches to learn multi-time-scale features jointly, accelerate the learning process, and reduce overfitting. This dissertation also compares the multi-branch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. The proposed multi-branch ResAttGRU requires 7.1% fewer parameters than multi-branch residual LSTM (ResLSTM) while achieving similar average RMSE, MAE, and R-squared values. Finally, to effectively model spatial correlation among neighboring solar sites as well as to alleviate performance degradation due to overfitting of conventional neural networks, a spatio-temporal framework comprised of concatenated multi-branch Residual network and Transformer (ResTrans) is developed. Numerical results indicate that the multi-branch ResTrans structure achieves the highest forecasting accuracy, with an average RMSE of 0.049 ( W/m^2 ), an average MAE of 0.031 (W/m^2 ), and a R^2 coefficient of 97%. / Electrical and Computer Engineering

<p>Climate change is recognized as one of the most significant challenge of the 21st century. Anthropogenic activities have led to a substantial increase in greenhouse gases (GHGs) since the Industrial Revolution, with the energy sector being one the biggest contributors globally. The energy sector is now facing unique challenges not only due to decarbonization goals but also due to increased risks of climate extremes under climate change. </p><p>This dissertation focuses on leveraging machine learning, specifically utilizing unstructured data such as images, to address many of the unprecedented challenges faced by the energy systems. The dissertation begins (Chapter 1) by providing an overview of the risks posed by climate change to modern energy systems. It then explains how machine learning applications can help with addressing these risks. By harnessing the power of machine learning and unstructured data, this research aims to contribute to the development of more resilient and sustainable energy systems, as described briefly below. </p><p>Accurate forecasting of generation is essential for mitigating the risks associated with the increased penetration of intermittent and non-dispatchable variable renewable energy (VRE). In Chapters 2 and 3, deep learning techniques are proposed to predict solar irradiance, a crucial factor in solar energy generation, in order to address the uncertainty inherent in solar energy. Specifically, Chapter 2 introduces a cost-efficient fully exogenous solar irradiance forecasting model that effectively incorporates atmospheric cloud dynamics using satellite imagery. Building upon the work of Chapter 2, Chapter 3 extends the model to a fully probabilistic framework that not only forecasts the future point value of irradiance but also quantifies the uncertainty of the prediction. This is particularly important in the context of energy systems, as it relates to high-risk decision making.</p><p>While the energy system is a major contributor to GHG emissions, it is also vulnerable to climate change risks. Given the essential role of energy systems infrastructure in modern society, ensuring reliable and sustainable operations is of utmost importance. However, our understanding of reliability analysis in electricity transmission networks is limited due to the lack of access to large-scale transmission network topology datasets. Previous research has mostly relied on proxy or synthetic datasets. Chapter 4 addresses this research gap by proposing a novel deep learning-based object detection method that utilizes satellite images to construct a comprehensive large-scale transmission network dataset.</p>