Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV) power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV...

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Bibliographic Details
Published in:International journal of photoenergy 2017-01, Vol.2017 (2017), p.1-9
Main Authors: Khan, Danish, Yao, Jianxi, Zhu, Honglu, Khan, Idris, Iqbal, Tahir
Format: Article
Language:English
Subjects:
Air quality
Alternative energy sources
Back propagation networks
Climatology
Greenhouse effect
Greenhouse gases
Haze
Indication
Mathematical models
Neural networks
Outdoor air quality
Photovoltaic cells
Solar cells
Solar radiation
Weather forecasting
Wind power generation
Wind speed
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Summary:High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV) power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI) is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP) neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.
ISSN:1110-662X
1687-529X
DOI:10.1155/2017/6938713