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

Idris Khan,Honglu Zhu,Jianxi Yao,Danish Khan,Tahir Iqbal

doi:10.1155/2017/6938713

Idris Khan, Honglu Zhu + Show 3 more

Open Access

https://doi.org/10.1155/2017/6938713

Copy DOI

Abstract

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.

Highlights

The solar energy is known as an ideal source of renewable energy power generation
The data are from photovoltaic Power Station of the State Key Laboratory of Electrical Power System with Renewable Energy Sources in North China Electric Power University (NCEPU) in Changping District, Beijing, with installed capacity of 3 KW and sampling interval of 15 minutes
The prediction results are evaluated through root mean square error (RMSE) and mean absolute error (MAE)

Summary

Introduction

Photovoltaic (PV) power generation is the main application pattern of solar energy, but the output of photovoltaic power station has high unpredictability, variation, and intermittent nature [1, 2]. Forecasting and energy scheduling are significant to ensure bulk power system reliability and dependable operations. PV power forecasting depends on solar radiation and other factors like humidity, wind speed, and temperature. Solar radiation at one location on the earth’s surface indicates periodicity and nonstationary characteristic due to the effect of the earth’s rotation and revolution. Output power data of photovoltaic power station indicates periodicity in one day. If effective procedure is not adopted to reduce nonstationary characteristic of PV output power, conventional power forecast technique will not guarantee precision of forecasting results and algorithm convergence [3]

Results

Discussion

Conclusion