Short-term forecasting of solar photovoltaic output power for tropical climate using ground-based measurement data

Baharin, Kyairul Azmi; Rahman, Hasimah Abdul; Hassan, Mohammad Yusri; Gan, Chin Kim

doi:10.1063/1.4962412

Cited by 27 publications

(13 citation statements)

References 54 publications

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“…In [54] the authors used a SVR-based method applied to a small-scale PV plant located in Melaka, Malaysia. The use of different input has been investigated including the tilted and horizontal global irradiance, and the module temperature.…”

Section: Application Of Machine Learning In Pv Power Forecastingmentioning

confidence: 99%

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

et al. 2020

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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.

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Section: Application Of Machine Learning In Pv Power Forecastingmentioning

confidence: 99%

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

et al. 2020

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“…In addition to solar irradiance estimation, there have been several efforts in forecasting the solar irradiance, with a lead time of few minutes. Baharin et al (2016) proposed a machine-learning forecast model for PV power output, using Malaysia as the case study. Similarly Chu et al (2015) used a reforecasting method to improve the PV power output forecasts with a lead time of 5, 10, and 15 minutes.…”

Section: Related Workmentioning

confidence: 99%

Estimating solar irradiance using sky imagers

2019

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Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations. IntroductionClouds have a significant impact on solar energy generation. They intermittently block the sun and significantly reduce the solar irradiance reaching solar panels. A short-term forecast of solar irradiance is needed for grid operators to mitigate the effects of a drop in power generation. With rapid developments in photogrammetric techniques, ground-based sky cameras are now widely used (Dev et al., 2016e). These cameras, known as Whole Sky Imagers (WSIs) are upward looking devices that capture images of the sky at regular intervals of time. These images are subsequently used for automatic cloud coverage computation, cloud tracking, and cloud base height estimation. In our research group, we use these imagers to study the effects of clouds on satellite communication links (Dev et al., 2018b;Yuan et al., 2016;Dev et al., 2017e).Localized and short-term forecasting of cloud movements is an on-going research topic (Shakya et al., 2017;Jiang et al., 2017;Feng et al., 2018). Optical flow techniques can be used to forecast images using anterior frames (Dev et al., 2016d).Similarly, cloud motion vectors are exploited for solar power prediction from satellite images (Jang et al., 2016). Our proposed method for estimating solar irradiance is thus a first step towards solar irradiance forecasting, as the input data used to estimate the irradiance is the same as the one used to forecast the sky condition.The accurate estimation and prediction of solar energy generation is a challenging task, as clouds greatly impact the total irradiance received on the earth's surface. In the event of clouds covering the sun for a short time, there is a sharp decline of the 1 arXiv:1910.04981v1 [eess.IV]

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“…If there is great confidence in the short‐term hydro, wind, and solar power generation forecast, it is feasible to forecast the LMP trend according to the above analysis results. For instance, if it is known that the joint fluctuation pattern of the last time is RRFR , given the fluctuation pattern of hydro, wind, and solar power generation of the next step (about 5 minutes later) is RFR , then it can be forecasted that the LMP fluctuation pattern of the next step is most likely to be R, followed by E .…”

Section: Data Collection and Processingmentioning

confidence: 99%

Research on the joint fluctuation laws between locational marginal price and renewables based on complex networks: A case study in Independent System Operator New England

Liu

Zhao

et al. 2019

Energy Science & Engineering

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The growing share of renewable energy has an increasingly significant impact on the electricity market. Locational marginal price (LMP), as a sensitive price signal, has a potential but considerable correlation with renewable energy generation. In this context, this paper focuses on the joint fluctuation laws between LMP and renewables consumed to generate electricity. At first, LMP and renewables consumption data from Independent System Operator New England (ISO‐NE) are collected and preprocessed. Then, through highly comparative time‐series analysis (hctsa), it is found that discrete symbolization features have effective description ability. Therefore, a coarse‐grained method is conducted to convert the real matrix data into a symbol vector based on numerical distribution analysis. Next, joint fluctuation complex networks are constructed according to the symbol vector. From a global perspective, the joint fluctuation network follows an exponential distribution and exhibits hierarchical modularity. From a local perspective, the joint fluctuation network follows a power‐law distribution. The research results of this paper provide a new perspective to understand the evolution of the electricity market with large‐scale renewable energy and have reference value for identifying the valuable joint fluctuation patterns and improving the LMP forecast results.

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Short-term forecasting of solar photovoltaic output power for tropical climate using ground-based measurement data

Cited by 27 publications

References 54 publications

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Estimating solar irradiance using sky imagers

Research on the joint fluctuation laws between locational marginal price and renewables based on complex networks: A case study in Independent System Operator New England

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