In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of n-RMSE (Normalized Root Mean Square Error), 48.04% of Max-AE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

https://www.mdpi.com/1999-5903/15/2/46/pdf?version=1676389463

Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.

/pdf/virtual-collection-for-distributed-photovoltaic-data-2w0djowa.pdf

Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

World leaders and scientists have been putting immense efforts to strengthen energy security and reducing greenhouse gas (GHG) emissions by meeting growing energy demand for the last couple of decades. Their efforts accelerate the need for large-scale renewable energy resources (RER) integration into existing electricity grids. The intermittent nature of the dominant RER, e.g., solar photovoltaic (PV) and wind systems, poses operational and technical challenges in their effective integration by hampering network reliability and stability. This article reviews and discusses the challenges reported due to the grid integration of solar PV systems and relevant proposed solutions. Among various technical challenges, it reviews the non-dispatch-ability, power quality, angular and voltage stability, reactive power support, and fault ride-through capability related to solar PV systems grid integration. Also, it addresses relevant socio-economic, environmental, and electricity market challenges. Finally, it highlights the proposed solution methodologies, including grid codes, advanced control strategies, energy storage systems, and renewable energy policies to combat the discussed challenges. The findings of this article assist the power system scholars and researchers in conducting further research in this field. Furthermore, it helps the decision-makers to choose the appropriate technologies to deal with the anticipated challenges associated with the grid integration of PV systems. 

Grid Integration Challenges and Solution Strategies for Solar PV Systems: A Review

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.

/pdf/forecasting-photovoltaic-power-generation-with-a-stacking-28i9najw.pdf

Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

/pdf/systematic-review-on-impact-of-different-irradiance-282w46s3.pdf

M Husain

Papers

Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach