This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine Learning algorithms and Conv2D Long Short-Term Memory model based Deep Learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 $R^2$ Score, 0.18 MAE, 0.10 RMSE with Conv2D Long Short-Term Memory model, showcasing the power transform technique's innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and Deep Learning techniques for solar energy prediction.

Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic systems in a region holds significant importance for various stakeholders in the energy sector. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous work has limited the focus to either forecasting a single time series (i.e., aggregated time series) which is the addition of all solar generation time series in a region, disregarding the location-specific weather effects or forecasting solar generation time series of each PV site (i.e., individual time series) independently using location-specific weather data, resulting in a large number of forecasting models. In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region. We propose two hierarchical temporal convolutional neural network architectures (HTCNN) and two strategies to adapt HTCNNs for regional solar power forecasting. At first, we explore generating a regional forecast using a single HTCNN. Next, we divide the region into multiple sub-regions based on weather information and train separate HTCNNs for each sub-region; the forecasts of each sub-region are then added to generate a regional forecast. The proposed work is evaluated using a large dataset collected over a year from 101 locations across Western Australia to provide a day ahead forecast. We compare our approaches with well-known alternative methods and show that the sub-region HTCNN requires fewer individual networks and achieves a forecast skill score of 40.2% reducing a statistically significant error by 6.5% compared to the best counterpart.

The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to changes in data distribution, resulting in a model that works well in one region but not in others. Furthermore, as a consequence of global warming, there is a notable acceleration in the alteration of weather patterns on an annual basis. This phenomenon introduces the potential for diminished efficacy of existing models, even within the same geographical region, as time progresses. In this paper, a domain adaptive deep learning-based framework is proposed to estimate solar power generation using weather features that can solve the aforementioned challenges. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. This adaptive data-driven approach exhibits notable advantages in terms of computing speed, storage efficiency, and its ability to improve outcomes in scenarios where state-of-the-art non-adaptive methods fail. Our method has shown an improvement of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.

The rapid growth of decentralized energy resources and especially Electric Vehicles (EV), that are expected to increase sharply over the next decade, will put further stress on existing power distribution networks, increasing the need for higher system reliability and flexibility. In an attempt to avoid unnecessary network investments and to increase the controllability over distribution networks, network operators develop demand response (DR) programs that incentivize end users to shift their consumption in return for financial or other benefits. Artificial intelligence (AI) methods are in the research forefront for residential load scheduling applications, mainly due to their high accuracy, high computational speed and lower dependence on the physical characteristics of the models under development. The aim of this work is to identify households' EV cost-reducing charging policy under a Time-of-Use tariff scheme, with the use of Deep Reinforcement Learning, and more specifically Deep Q-Networks (DQN). A novel end users flexibility potential reward is inferred from historical data analysis, where households with solar power generation have been used to train and test the designed algorithm. The suggested DQN EV charging policy can lead to more than 20% of savings in end users electricity bills.

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.

Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to address the challenge of domestic EV charging while prioritizing clean, solar energy consumption. Real Time-of-Use tariffs are treated as a price-based Demand Response (DR) mechanism that can incentivize end-users to optimally shift EV charging load in hours of high solar PV generation with the use of Deep Reinforcement Learning (DRL). Historical measurements from the Pecan Street dataset are analyzed to shape a flexibility potential reward to describe end-user charging preferences. Experimental results show that the proposed DQN EV optimal charging policy is able to reduce electricity bills by an average 11.5\% by achieving an average utilization of solar power 88.4

Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power.