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Solar Power Forecasting Using Adaptive Curve-Fitting Algorithm

  • N. SampathrajaEmail author
  • L. Ashok Kumar
  • R. Saravana Kumar
  • I. Made Wartana
Conference paper
  • 36 Downloads

Abstract

Electricity is generated from different sources such as thermal, coal, nuclear, solar, wind and so on. The generated electricity is connected to grids for further use. If forecast from renewable energy is available, then the utilization of non-renewable resources could be reduced and so the cost and impact on environment can also be reduced through optimized grid balancing. Solar power is one of the renewable power sources in focus due to the upgradation of photovoltaic technologies and simplified system components. But, the yield out of photovoltaic cells could be strongly influenced by factors such as shadow, cloud, rain, dust, temperature, humidity, panel angle, seasonal effects, panel efficiency and so on. Hence, all these factors need to be considered for forecasting solar power. In this chapter, the ‘Adaptive Curve Fitting’ model is introduced for forecasting solar power, wherein the majority of the algorithm is based on mathematical modelling which considers clear sky reference power, real time power and optionally the weather prediction data.

Keywords

Clear sky reference Time segmentation Slope of the reference power Real time power Cloud effect 

Abbreviations

ACF

Adaptive Curve Fitting

NPF

Next Day Forecast Power

SAP

Same Day Actual Power

SFP

Same Day Forecast Power

Tu

Time Unit

References

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • N. Sampathraja
    • 1
    Email author
  • L. Ashok Kumar
    • 1
  • R. Saravana Kumar
    • 1
  • I. Made Wartana
    • 2
  1. 1.Department of Electrical and Electronics EngineeringPSG College of TechnologyCoimbatoreIndia
  2. 2.Department of Electrical EngineeringITN MalangMalangIndonesia

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