The use of data-driven ensemble approaches for the prediction of the solar Photovoltaic (PV) power production is promising due to their capability of handling the intermittent nature of the solar energy source. In this work, a comprehensive ensemble approach composed by optimized and diversified Artificial Neural Networks (ANNs) is proposed for improving the 24h-ahead solar PV power production predictions. The ANNs are optimized in terms of number of hidden neurons and diversified in terms of the diverse training datasets used to build the ANNs, by resorting to trial-and-error procedure and BAGGING techniques, respectively. In addition, the Bootstrap technique is embedded to the ensemble for quantifying the sources of uncertainty that affect the ensemble models' predictions in the form of Prediction Intervals (PIs). The effectiveness of the proposed ensemble approach is demonstrated by a real case study regarding a gridconnected solar PV system (231 kWac capacity) installed on the rooftop of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. The results show that the proposed approach outperforms three benchmark models, including smart persistence model and single optimized ANN model currently adopted by the PV system's owner for the prediction task, with a performance gain reaches up to 11%, 12%, and 9%, for RMSE, MAE, and WMAE standard performance metrics, respectively. Simultaneously, the proposed approach has shown superior in quantifying the uncertainty affecting the power predictions, by establishing slightly wider PIs that achieve the highest confidence level reaches up to 84% for a predefined confidence level of 80% compared to three other approaches of literature. These enhancements would, indeed, allow balancing power supplies and demands across centralized grid networks through economic dispatch decisions between the energy sources that contribute to the energy mix. INDEX TERMS Artificial neural networks, ensemble, photovoltaic power, prediction, bootstrap, uncertainty quantification.

Abstract. The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminum electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature. Keywords

Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h=[1,24] (i.e., an interval of ∆ℎ=1 hour), are exploited to build, optimize, and evaluate H=24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H=1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., ∆ℎ=2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H =12, 8, and 6 models, respectively).

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task.

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The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kW ac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2) performance metrics, respectively, compared to the Persistence prediction model of literature.

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This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R2 of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated.