The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k‐nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross‐validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs.

Recently, with the birth of globalization, the world has witnessed a huge growth in its energy consumption. From an agricultural society, the world has transformed itself into an industrial and knowledge society. This transformation leads to a surge in energy consumption which led to an increase in carbon emission. For this reason, renewable energy systems with zero carbon emission has become a vital need for economic comfort and environmental security. However, the main disadvantage of renewable energy is its intermittency and prediction. Solar and wind generation are usually unpredictable, which leads to several problems if the network heavily relies on renewable energy as the primary source of electricity. This chapter describes the integration of the internet of things (IoT) with renewable energy systems to cover these problems. The authors will start with an introduction to renewable energy systems and their limitations. Then, they focus on the advantage of using IoT to enhance renewable systems.

The increasing energy demand, driven by population growth and technological advancements, necessitates a shift from traditional fossil fuel-based power generation to renewable energy systems. Solar photovoltaic panels offer a promising alternative; however, their energy output fluctuations present challenges in maintaining a reliable power source. Accurate estimation of solar energy generation is critical for enhancing the competitiveness of solar power plants and fostering social and economic progress. This paper aims to develop a machine learning model that utilizes meteorological and time-related data to predict solar photovoltaic power generation. It investigates how various factors, such as humidity and seasonal variations, impact prediction accuracy. Three different ML models were used and compared: Support vector regression, XGBoost, and HistGradient Boost. To improve the performance of these models hyperparameter tuning techniques is used. The findings reveal that radiation, humidity levels, and seasonal variations are the most influential factors in predicting photovoltaic solar power generation. Additionally, incorporating seasonal and humidity data can enhance the machine-learning model’s accuracy by 17.9%. XGBoost model outperforms existing methods, achieving the highest accuracy of 95.76% when incorporating humidity and seasonal factors. Its lowest MAPE of 4.23 and highest $R^{2}$ of 99.1. to assess model precision, an uncertainty analysis was performed using p-factor and q-factors. The results confirm model robustness and reliability in predictive performance.

Decision-making systems are computer-based systems that interpret processed information to make the best choice. They choose an action or value from possible activities, each conducting to different outcomes. Some outcomes are favored over others based on the criteria of the decision maker. This article presents the existing technology used in decision-making systems. The authors focus then on explaining how to make the optimal decision based on the available information and criteria. The authors conclude that decision techniques are chosen based on the type of application and the quality of available data. Some techniques are simple but not accurate such as the fishbone and five whys techniques. Other methods, such as decision trees, are more complex to build, but unlike a fishbone diagram, it assists humans in evaluating upcoming choices. It forces the examination of all available outcomes of a decision and traces a possible path to the best alternative.