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The rapid development of smart renewable energy grids (SREGs) has resulted in a vast amount of data that requires efficient access control and secure mechanisms for sharing energy records among stakeholders. This paper proposes a novel approach called the identity‐based proxy signcryption‐based scheme for SREGs (ID‐PSC‐SREGs), which ensures the secure sharing of energy records in SREGs. The ID‐PSC‐SREG scheme integrates the benefits of signature and encryption techniques, merging them into a unified algorithm and providing a comprehensive solution for the confidentiality and authenticity of energy records. Extensive security analysis demonstrates that the scheme achieves provable security against adaptive chosen ciphertext attacks (IND‐ID‐PSC‐SREG‐CCA2) and existential unforgeability against adaptive chosen message attacks (EUF‐ID‐PSC‐SREG‐CMAs) under the decisional Diffie–Hellman problem. In order to further ascertain the security of the ID‐PSC‐SREG scheme, formal verification utilizing the automated validation of internet security protocols and applications (AVISPAs) is performed. The results confirm the scheme's safety under the On‐the‐Fly Model‐Checker (OFMC) and Constraint Logic‐based Attack Searcher (CL‐AtSe). [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications. [ABSTRACT FROM AUTHOR]

The decaying prices and improving efficiency of bifacial solar photovoltaic (PV) technologies make them most promising for harnessing solar radiation. Deserts have a high solar potential, but harsh conditions like high temperatures and dust negatively affect the performance of any proposed solar system. The most attractive aspect of deserts is their long-term sustainability, as they are free from urban and agricultural expansion. In this work, the System Advisor Model (SAM) software version 2023.12.17 was used to model a 100 MW PV plant and evaluate the techno-economic performance of fixed, 1-axis, and 2-axis bifacial systems under the climatic conditions of six deserts from around the world. This study explores technical parameters such as the performance ratio, specific yield, and capacity factor. Additionally, the levelized cost of energy (LCOE) indicator was used to compare the economic performance of the different systems. Results showed high specific yield: the averages for the three systems in six deserts were 2040, 2372, and 2555 kWh/kWp, respectively. Economic analysis found that an LCOE below 4 ¢/kWh is achievable in all deserts, reaching a minimum of 2.45 ¢/kWh under favorable conditions. These results emphasize the high potential of utility-scale PV projects in deserts to advance a green, sustainable energy future. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature. [ABSTRACT FROM AUTHOR]

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels' most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences. [ABSTRACT FROM AUTHOR]

Accurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high‐reliability equipment, few complete‐run‐to‐failure trajectories may be available in practice. This constitutes a technical challenge for data‐driven techniques for estimating the RUL. This paper proposes a novel data‐driven approach for fault prognostics using the Long‐Short Term Memory (LSTM) model combined with the Multi‐Head Self‐Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long‐term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high‐reliability equipment compared to four benchmark models from the literature. [ABSTRACT FROM AUTHOR]