Photovoltaic power forecasting with a rough set combination method

Yang, Xiyun; Ren, Jie; Yue, Hong

doi:10.1109/control.2016.7737652

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…The solar power forecasting output is assumed to be similar to the power value measured on last or coming day [23]. The forecasted output for the next 24 h can be expressed as [45]…”

Section: Persistence Modelmentioning

confidence: 99%

“…The solar power forecasting output is assumed to be similar to the power value measured on last or coming day [23]. The forecasted output for the next 24 h can be expressed as [45]

P_{f} ()(, t + h) = P_{pd} false(t false)

where

P_{f} ()(, t + h)

is the forecasted power and

P_{pd} false(t false)

is the power output of the day prior to the forecasted day, at the same time. This method is not suitable for forecasting >1 h and can only be used for comparative analysis with other advanced techniques [43].…”

Section: Mathematical Forecasting Techniquesmentioning

confidence: 99%

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

Akhter

Mekhilef

Mokhlis

et al. 2019

IET Renewable Power Generation

334

147

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The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

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“…The solar power forecasting output is assumed to be similar to the power value measured on last or coming day [23]. The forecasted output for the next 24 h can be expressed as [45]…”

Section: Persistence Modelmentioning

confidence: 99%

“…The solar power forecasting output is assumed to be similar to the power value measured on last or coming day [23]. The forecasted output for the next 24 h can be expressed as [45]

P_{f} ()(, t + h) = P_{pd} false(t false)

where

P_{f} ()(, t + h)

is the forecasted power and

P_{pd} false(t false)

Section: Mathematical Forecasting Techniquesmentioning

confidence: 99%

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

Akhter

Mekhilef

Mokhlis

et al. 2019

IET Renewable Power Generation

334

147

View full text Add to dashboard Cite

show abstract

“…However, this model's accuracy depends on the stability of the input data, for example, today's weather conditions should be the same as that of yesterday. If there is sudden changes, the forecasting error becomes larger [56].…”

Section: Solar Output Forecastingmentioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined.

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“…The major difference between these two techniques is that the direct method does not need the value of the solar irradiance to be calculated at an intermediate stage. Image-based, numerical weather prediction (NWP), artificial neural network (ANN), and hybrid ANN have used indirect forecasting methods on different time scales to forecast solar PV output power [34][35][36][37][38][39][40][41][42][43][44]. Many commercial PV output power simulation software, such as PVSol [45], PVsyst [46], PVGIS, PVWatts and RETScreen [15], and Helioscope [47], have used these methods to forecast PV power generation.…”

Section: Direct and Indirect Forecasting Modelsmentioning

confidence: 99%

Solar Photovoltaic Power Forecasting: A Review

Iheanetu

2022

Sustainability

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The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.

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Photovoltaic power forecasting with a rough set combination method

Cited by 16 publications

References 23 publications

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

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

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Solar Photovoltaic Power Forecasting: A Review

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