Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants

Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance

Economy of grid-connected photovoltaic systems and comparison of irradiance/electric power predictions vs. experimental results

This thesis is focused on various aspects concerning the Distributed Generation (DG) from Renewable Energy Sources (RES) and in particular from PhotoVoltaics (PV). The PV generation strongly depends on weather conditions (irradiance and temperature), therefore the solar irradiance forecast is very important for grid-connected PV systems. The PV power injected into the grid is concentrated during sunlight hours, in which the maximum peak load demand occurs and, as a consequence, an impact on the electrical system occurs. The task of the Transmission System Operator (TSO) is to ensure a constant balance between supply and consumption within the grid. Therefore, the presence of strong fluctuations of the solar radiation requires additional regulatory actions and compensation, through the use of short-term power backup, causing an increase in network costs. Thus, the solar irradiance forecast is necessary for an accurate evaluation of the PV power from PV systems, for the management of electrical grids in order to minimize the costs of energy imbalance and for the decisions concerning the energy market. This thesis essentially consists of two parts. In the first part, the profitability of investments in the rooftop grid-connected PV systems subjected to incentive and the grid-parity analysis in the two main European PV markets (Italy and Germany) are presented. In the second part, in order to minimize the costs of energy imbalance in the Italian electricity market, the comparison of irradiance and electric power predictions with respect to the experimental results of grid-connected PV systems is presented

Impact of data quality on photovoltaic (PV) performance assessment

In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or meteorological data in monitoring. An automatic detection of wrong input information such as system nominal capacity and azimuth is developed, based on statistical distributions of annual figures of PV system performance ratio (PR) and final yield. This approach is specifically useful in carrying out PV fleet analyses where only monthly or annual energy outputs are available. The evaluation is carried out based on synthetic weather data which is obtained by interpolating from a network of about 80 meteorological monitoring stations operated by the UK Meteorological Office. The procedures are used on a large PV domestic dataset, obtained by a social housing organisation, where a significant number of cases with wrong input information are found. Data interruption is identified as another challenge in PV monitoring data, although the effect of this is particularly under-researched in the area of PV. Disregarding missing energy generation data leads to falsely estimated performance figures, which consequently may lead to false alarms on performance and/or the lack of necessary requirements for the financial revenue of a domestic system through the feed-in-tariff scheme. In this work, the effect of missing data is mitigated by applying novel data inference methods based on empirical and artificial neural network approaches, training algorithms and remotely inferred weather data. Various cases of data loss are considered and case studies from the CREST monitoring system and the domestic dataset are used as test cases. When using back-filled energy output, monthly PR estimation yields more accurate results than when including prolonged data gaps in the analysis. Finally, to further discriminate more obscure data from system faults when higher temporal resolution data is available, a remote modelling and failure detection framework is ii developed based on a physical electrical model, remote input weather data and system description extracted from PV module and inverter manufacturer datasheets. The failure detection is based on the analysis of daily profiles and long-term PR comparison of neighbouring PV systems. By employing this tool on various case studies it is seen that undetected wrong data may severely obscure fault detection, affecting PV system s lifetime. Based on the results and conclusions of this work on the employed residential dataset, essential data requirements for domestic PV monitoring are introduced as a potential contribution to existing lessons learnt in PV monitoring

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency

Forecasting photovoltaic power generation with a stacking ensemble model

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

Review and Analysis on Solar Energy Forecasting Using Soft Computing and Machine Learning Methodologies

Traditional power producing methods can't keep pace with India's growing need for electricity. New Delhi to Kolkata were all without power as of July 30, 2012, due to the world's largest blackout. In the next five years, India's power generation capacity will expand by 44 percent. Demand for power develops as India's population and economy expand. To reduce power outages and satisfy future energy needs, what needs to be changed? India has made the decision to move away from fossil fuels in favor of renewable energy sources, both for economic and environmental reasons. There has been an increase in the use of solar PV panels as a sustainable energy source in recent years. With improved access to data and computing power, machine-learning algorithms can now make better predictions. Machine learning and time series models can assist many stakeholders in the energy industry make accurate projections of solar PV energy output. In this study, various machine learning algorithms and time series models are evaluated to find which is most effective. While much research has already gone into wind energy forecasting, solar energy forecasting is only now beginning to see an uptick in interest. A detailed review and analysis model is presented in this study. Power system operational planning has become a major issue in today's world. In order for the power system to function properly, a range of factors must be anticipated with the utmost accuracy over various forecasting horizons. It is important to note, however, that scholars have devised a variety of methods for forecasting distinct factors. Exogenous variables play an important role in the implementation and analysis of new forecasting models that have recently been published in the literature. In order to predict renewable energy resources, an intelligent approach is needed. Achieving the best accurate forecasts for these variables while minimizing computing effort is a work in progress because of the rising complexity of the power system. Solar power forecasting as well as wind power forecasting will be the focus of this research in light of these concerns. Comparing these models' outcomes to the results of previous models will also be done

Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring