Forecasting of photovoltaic power generation and model optimization: A review

Das, Utpal Kumar; Soon, Tey Kok; Seyedmahmoudian, Mehdi; Mekhilef, Saad; Idris, Moh Yamani Idna; Deventer, Willem Van; Horan, Ben; Stojcevski, Alex

doi:10.1016/j.rser.2017.08.017

Cited by 857 publications

(478 citation statements)

References 107 publications

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“…Physical methods, mainly used for applications ranging from very short to long horizons, consist of a set of mathematical equations that describe the physical state and dynamic motion of the atmosphere [35]. These methods are mainly based on Numerical Weather Prediction (NPW), Sky Imagery and Satellite Imaging [36]. They can be classified as global and mesoscale physical methods, based on the portion of the simulated atmosphere-which can be at a worldwide scale or include a limited area [37].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

et al. 2019

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We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.Energies 2019, 12, 1621 2 of 15 measurements and data from weather forecast providers [7] are needed in order to optimize the operation of these systems. The benefits of such models are manifold. Considering the point of view of the owners of residential and Commercial-Industrial (C&I) PV plants, and specifically the possibility of operating in a grid-parity regime or better, accurate forecasts enable the maximization of the self-consumed energy and minimization of the Levelized Cost of the Energy produced (LCoE) [8,9]. Also, when residential and C&I systems operate as part of a microgrid, the power forecasts can be used as input for the Energy Management System (EMS) used to optimize the State of Charge (SoC) of the storage devices [10]. On the other hand, considering larger-scale plants and fuel parity [11], the managers of utility-scale PV plants use the forecasts to optimally plan the downtime of plants for maintenance purposes. Moreover, in countries with a day-ahead electricity market, forecast models allow for optimization of the schedule for the supply offers on the market, avoiding penalties and reduced revenues [12,13]. On the other hand, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) also need reliable forecasts in order to handle the uncertainty and fluctuations of the PV distributed generators connected to their grids. The availability of reliable forecasts allows DSOs and TSOs to cope with the intermittent nature of PV plants, avoiding problems in balancing power generation and load demand [14], enhancing the stability of the system, and reducing the cost of ancillary services [15,16]. Accurate forecasts enhance reliability, and reduce costs by allowing effi...

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Section: Introductionmentioning

confidence: 99%

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

et al. 2019

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“…Another study established in Malaysia to study the performance of two decentralized hybrid PV/diesel generators/batteries stations. The results demonstrate the significant role of analyzing the operational performance of hybrid renewable energy systems in order to avoid the disadvantages and get all proposed advantages associated with such systems [13][14][15]. The literature shows the important of studying the performance of the PV systems.…”

Section: Introductionmentioning

confidence: 89%

Performance Analysis of Multi-Photovoltaic (PV)-Grid Tied Plant in Malaysia

Halabi

Mekhilef

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. This paper evaluates and analyzes the performance of three different types of photovoltaic (PV) arrays connected to utility-grid without any storage system in Malaysia. The three types modules include Poly-Crystalline, Mono-Crystalline and Thin-Film modules, each works separately and are connected to three DC/AC inverters where it monitored and controlled over an IP network system. The system installed and monitored for one-year period and this is the first work describing such systems of different PV types at the same plant in the proposed location. The system continuously collects and stores all data sets of DC/AC current, voltage, frequency and power for a 5-minutes period. Besides, the weather parameters that include ambient temperature, module temperature, solar irradiation and wind speed. The performance of this plant is evaluated by analyzing the collected data sets and then applying performance indices such as final energy output, array yield, final yield, reference yield, system efficiency and performance ratio. The results show that the PV performance is depending directly on the solar irradiation as well as the surrounding environmental conditions. Also, the results show a very-good efficiency of the proposed modules and demonstrate the potential of using such systems in any area with the similar conditions.

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“…Energy production by Photovoltaic (PV) systems is one of the significant clean energy sources that covers part of the increasing energy demand with the ongoing industrial growth [1]. Nowadays, many factors contributed to world energy problems, either a supply-demand or economic problems, among those factors are increasing world population, increasing living standards (directly related to energy consumption per capita), industrialization and modernization [2]. All these factors induced a global trend to utilize more renewable energy sources into countries' energy mix.…”

Section: Introductionmentioning

confidence: 99%

PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

Alomari¹,

Adeeb²,

Younis³

2019

IJECE

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In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records' time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.

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Forecasting of photovoltaic power generation and model optimization: A review

Cited by 857 publications

References 107 publications

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Performance Analysis of Multi-Photovoltaic (PV)-Grid Tied Plant in Malaysia

PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

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