Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT - YOLO, a lightweight artificial intelligence (AI) model that integrat es an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone - based thermal inspections of PV systems, addressing the unique challenges of detecting small and subtl e thermal anomalies, such as hotspots and defective modules, while maintaining real - time performance. Experimental results demonstrate a mean a verage p recision of 90.8%, reflecting a significant improvement over baseline object detection models. With a reduced computational load and robustness under diverse environmental conditions, HOTSPOT - YOLO offers a scalable and reliable solution for large - scale PV inspections. This work highlights the integration of advanced AI techniques with practical engineering ap plications, revolutionizing automated fault detection in renewable energy systems.

Peter Palensky, Simon H. Tindemans Electrical Sustainable Energy Delft University of T echnology Delft, Netherlands { P .Palensky, S.H.Tindemans}@tudelft.nl Abstract --As the number of rooftop photovoltaic (PV) installations increases, aggregators and system operators are required to monitor and analyze these systems, raising the challenge of integration and management of large, spatially distributed time-series data that are both high-dimensional and affected by missing values. In this work, a probabilistic entity embedding-based clustering framework is proposed to address these problems. Applied to a multi-year residential PV dataset, it produces concise, uncertainty-aware cluster profiles that outperform a physics-based baseline in representativeness and robustness, and support reliable missing-value imputation. A systematic hyperparameter study further offers practical guidance for balancing model performance and robustness. I NTRODUCTION Modern energy systems are undergoing a rapid transformation, increasingly driven by decentralized generation sources, especially rooftop photovoltaic (PV) systems installed across residential and commercial properties.

We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed throughout the facility are organized in a hierarchical metering structure with multiple sub-metering levels, which is reflected in the dataset. The dataset contains measurement data from 72 energy meters, 9 heat meters and a weather station. Both raw and processed data at different processing levels, including labeled issues, is available. In this paper, we describe the data acquisition and post-processing employed to create the dataset. The dataset enables the application of a wide range of methods in the domain of energy management, including optimization, modeling, and machine learning to optimize building operations and reduce costs and carbon emissions.

Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and control consumption and generation in the grid. In order to meet the challenges, measurements within the distribution grid often form the basis for DSOs. Hence, it is an urgent problem that measurement devices are not installed in many low-voltage (LV) grids. In order to overcome this problem, we present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models. The feeder metadata comprise information about the number of grid connection points, the installed power of consumers and producers, and billing data in the downstream LV grid. Additionally, we use weather data, calendar data and timestamp information as model features. The existing measurements are used as model target. We extensively evaluate the estimated pseudo-measurements on a large real-world dataset with 2,323 LV feeders characterized by both consumption and feed-in. For this purpose, we introduce peak metrics inspired by the BigDEAL challenge for the peak magnitude, timing and shape for both consumption and feed-in. As regression models, we use XGBoost, a multilayer perceptron (MLP) and a linear regression (LR). We observe that XGBoost and MLP outperform the LR. Furthermore, the results show that the approach adapts to different weather, calendar and timestamp conditions and produces realistic load curves based on the feeder metadata. In the future, the approach can be adapted to other grid levels like substation transformers and can supplement research fields like load modeling, state estimation and LV load forecasting.

The intermittent nature of photovoltaic (PV) solar energy, driven by variable weather, leads to power losses of 10-70% and an average energy production decrease of 25%. Accurate loss characterization and fault detection are crucial for reliable PV system performance and efficiency, integrating this data into control signal monitoring systems. Computational modeling of PV systems supports technological, economic, and performance analyses, but current models are often rigid, limiting advanced performance optimization and innovation. Conventional fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. Artificial intelligence (AI), especially machine learning algorithms, offers improved fault detection by analyzing relationships between input parameters (e.g., meteorological and electrical) and output metrics (e.g., production). Once trained, these models can effectively identify faults by detecting deviations from expected performance. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm that processes meteorological, operational, and technical data. An artificial neural network (ANN) trained on synthetic datasets with a five-minute resolution simulates real-world PV system faults. A dynamic threshold definition for fault detection is based on historical data from a PV system at Universidad de los Andes. Key contributions include: (i) a PV system model with a mean absolute error of 6.0% in daily energy estimation; (ii) dynamic loss quantification without specialized equipment; (iii) an AI-based algorithm for technical parameter estimation, avoiding special monitoring devices; and (iv) a fault detection model achieving 82.2% mean accuracy and 92.6% maximum accuracy.

The photovoltaics (PV) technology landscape is evolving rapidly. To predict the potential and scalability of emerging PV technologies, a global understanding of these systems' performance is essential. Traditionally, experimental and computational studies at large national research facilities have focused on PV performance in specific regional climates. However, synthesizing these regional studies to understand the worldwide performance potential has proven difficult. Given the expense of obtaining experimental data, the challenge of coordinating experiments at national labs across a politically-divided world, and the data-privacy concerns of large commercial operators, however, a fundamentally different, data-efficient approach is desired. Here, we present a physics-guided machine learning (PGML) scheme to demonstrate that: (a) The world can be divided into a few PV-specific climate zones, called PVZones, illustrating that the relevant meteorological conditions are shared across continents; (b) by exploiting the climatic similarities, high-quality monthly energy yield data from as few as five locations can accurately predict yearly energy yield potential with high spatial resolution and a root mean square error of less than 8 kWhm$^{2}$, and (c) even with noisy, heterogeneous public PV performance data, the global energy yield can be predicted with less than 6% relative error compared to physics-based simulations provided that the dataset is representative. This PGML scheme is agnostic to PV technology and farm topology, making it adaptable to new PV technologies or farm configurations. The results encourage physics-guided, data-driven collaboration among national policymakers and research organizations to build efficient decision support systems for accelerated PV qualification and deployment across the world.

Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline which incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data. Using actual production data from hundreds of sites in the Netherlands, Australia and Belgium, we show that SolNet improves forecasting performance over data-scarce settings as well as baseline models. We find transfer learning benefits to be the strongest when only limited observational data is available. At the same time we provide several guidelines and considerations for transfer learning practitioners, as our results show that weather data, seasonal patterns, amount of synthetic data and possible mis-specification in source location, can have a major impact on the results. The SolNet models created in this way are applicable for any land-based solar photovoltaic system across the planet where simulated and observed data can be combined to obtain improved forecasting capabilities.

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.

We propose a novel Spatio-Temporal Graph Neural Network empowered trend analysis approach (ST-GTrend) to perform fleet-level performance degradation analysis for Photovoltaic (PV) power networks. PV power stations have become an integral component to the global sustainable energy production landscape. Accurately estimating the performance of PV systems is critical to their feasibility as a power generation technology and as a financial asset. One of the most challenging problems in assessing the Levelized Cost of Energy (LCOE) of a PV system is to understand and estimate the long-term Performance Loss Rate (PLR) for large fleets of PV inverters. ST-GTrend integrates spatio-temporal coherence and graph attention to separate PLR as a long-term "aging" trend from multiple fluctuation terms in the PV input data. To cope with diverse degradation patterns in timeseries, ST-GTrend adopts a paralleled graph autoencoder array to extract aging and fluctuation terms simultaneously. ST-GTrend imposes flatness and smoothness regularization to ensure the disentanglement between aging and fluctuation. To scale the analysis to large PV systems, we also introduce Para-GTrend, a parallel algorithm to accelerate the training and inference of ST-GTrend. We have evaluated ST-GTrend on three large-scale PV datasets, spanning a time period of 10 years. Our results show that ST-GTrend reduces Mean Absolute Percent Error (MAPE) and Euclidean Distances by 34.74% and 33.66% compared to the SOTA methods. Our results demonstrate that Para-GTrend can speed up ST-GTrend by up to 7.92 times. We further verify the generality and effectiveness of ST-GTrend for trend analysis using financial and economic datasets.

The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.