Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Akhter, Muhammad Naveed; Mekhilef, Saad; Mokhlis, Hazlie; Shah, Noraisyah Mohamed

doi:10.1049/iet-rpg.2018.5649

Cited by 335 publications

(178 citation statements)

References 141 publications

(256 reference statements)

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“…These variables make PV generation intermittent and stochastic. Therefore, large-scale PV power penetration in the utility grid requires reliable forecasting models to operate the power grid economically and reliably [1], [2]. The short term PV power forecast, which extends from an hour ahead to 24 hours ahead, is essential for a secured grid operation [1].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

2020

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In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power generation using a long short term memory (LSTM) neural network (NN). A synthetic weather forecast is created for the targeted PV plant location by integrating the statistical knowledge of historical solar irradiance data with the publicly available type of sky forecast of the host city. To achieve this, a K-means algorithm is used to classify the historical irradiance data into dynamic type of sky groups that vary from hour to hour in the same season. In other words, the types of sky are defined for each hour uniquely using different levels of irradiance based on the hour of the day and the season. This can mitigate the performance limitations of using fixed type of sky categories by translating them into dynamic and numerical irradiance forecast using historical irradiance data. The proposed synthetic weather forecast is proved to embed the statistical features of the historical weather data, which results in a significant improvement in the forecasting accuracy. The performance of the proposed model is investigated using different intraday horizon lengths in different seasons. It is shown that using the synthetic irradiance forecast can achieve up to 33% improvement in accuracy in comparison to that when an hourly categorical type of sky forecast is used, and up to 44.6% in comparison to that when a daily type of sky forecast is used. This highlights the significance of utilizing the proposed synthetic forecast, and promote a more efficient utilization of the publicly available type of sky forecast to achieve a more reliable PV generation prediction. Moreover, the superiority of the LSTM NN with the proposed features is verified by investigating other machine learning engines, namely the recurrent neural network (RNN), the generalized regression neural network (GRNN) and the extreme learning machine (ELM). INDEX TERMS PV power forecasting, machine learning, LSTM, neural network, deep learning, synthetic weather forecast.

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Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

2020

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“…Spatially distributed solar radiation information with a relatively high temporal resolution [90] obtained from meteorological satellites, for example, Himawari-8 [91], ‡ which have been undergoing remarkable technological innovation in recent years, contributes significantly to monitoring solar power generation for effective use. The methodology of PV power forecast using such information also has been actively discussed [92][93][94][95][96][97][98]; in particular, several recent studies have focused on the forecast of net-load that considers the effect of behind-the-meter PVs [99,100], which are necessary because of restrictions on the measurement location in real-world power systems. In addition, the situation is similar for wind power generation [101], although it is rare for largescale installations in cities; in the context of grasping wind power generation, the use of data via supervisory control and data acquisition (SCADA) system and the sophistication of numerical weather models [102] play important roles.…”

Section: Grasping and Forecasting Energy Fluctuationsmentioning

confidence: 99%

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Fujimoto

Ishii

Hayashi

2020

IEEJ Transactions Elec Engng

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Smart cities will be an important component for the construction of a sustainable future society. This paper focuses on the functionalities of smart cities from the perspective of electric power systems and reviews recent works on the various types of energy management systems (EMSs), particularly those focusing on the interaction among systems distributed in a city. Furthermore, this paper describes the idea of a platform for evaluating cooperative EMS technologies distributed in a city. The related research and demonstration of the EMS technology in real cities that were evaluated using the proposed platform are presented. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…Among the all existing predictive algorithm, hybrid algorithms are found a high effective prediction of PV power with nonlinear system. 26 Prediction of power is not mainly applicable to PV, it also made in wind energy. Secondly called renewable energy to support a rise in demand is wind energy.…”

Section: Generation Of Renewable Energy Forecastingmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Cited by 335 publications

References 141 publications

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Deep learning and reinforcement learning approach on microgrid

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