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Research Papers

A Novel Model for Daily Energy Production Estimation of Grid-Connected Photovoltaic System

[+] Author and Article Information
Li Fen

School of Electrical Engineering,
Shanghai University of Electric Power,
Changyang Road No. 2588,
Yangpu District,
Shanghai 200090, China
e-mail: beckyhust@163.com

Yan Quanquan

Maintenance Company of SMEPC,
Wuning Road No. 600,
Putuo District,
Shanghai 200063, China
e-mail: earthnet@foxmail.com

Duan Shanxu

State Key Laboratory of Advanced
Electromagnetic Engineering and Technology,
Huazhong University of Science and Technology,
Luoyu Road No. 1037,
Wuhan 430074, China
e-mail: duanshanxu@hust.edu.cn

Zhao Jinbin

School of Electrical Engineering,
Shanghai University of Electric Power,
Changyang Road No. 2588,
Yangpu District,
Shanghai 200090, China
e-mail: zhaojinbin@shiep.edu.cn

Ma Nianjun

School of Electrical Engineering,
Shanghai University of Electric Power,
Changyang Road No. 2588,
Yangpu District,
Shanghai 200090, China
e-mail: mnj0604@126.com

Chen Zhenghong

Hubei Provincial
Meteorological Service Center,
Donghu Road No. 3,
Wuhan 430074, China
e-mail: chenzh64@126.com

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received August 18, 2014; final manuscript received December 4, 2014; published online January 8, 2015. Assoc. Editor: Santiago Silvestre.

J. Sol. Energy Eng 137(3), 031013 (Jun 01, 2015) (8 pages) Paper No: SOL-14-1233; doi: 10.1115/1.4029454 History: Received August 18, 2014; Revised December 04, 2014; Online January 08, 2015

The rapidly growing markets for distributed and centralized grid-connected photovoltaic (PV) systems require the reliable and available information for reflecting and predicting the electricity generation of PV systems. In this work, the relationship between PV energy production and meteorological environmental factors is discussed by correlation analysis and partial correlation analysis. Meteorological data available, including the clearness index, diurnal temperature range, the global radiation on horizontal surface, and etc., are used as inputs. Then, according to factor analysis, these various interaction factors are extracted as two independent common factors. Finally, a new method based on factor analysis and multiple regression analysis has been developed for estimating the daily PV energy production. The meteorological data are collected from Wuhan Observatory, and power data from a roof grid-connected PV system located at Huazhong University of Science and Technology in Wuhan. The data of the whole year (from March in 2010 to February in 2011) has been used for model calibration and the following data of March in 2011 is used to test the predictions. The results show that there is significant positive correlation between the estimated values and the measured values; the rMBE per day is −0.14%, MAPE per day is 13.60% and rRMSE per day is 18.04%.

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References

Figures

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Fig. 1

The grid-connected PV system and local meteorological measuring system

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Fig. 2

Variations of daily PV energy production and clearness index in a typical month of January, April, July, and October

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Fig. 3

The relationship between daily PV energy production, global radiation on horizontal surface, sunshine duration in typical month of January, April, July, and October: (a) E and H and (b) E and S

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Fig. 4

The relationship between daily PV energy production and diurnal temperature range in a typical month of January, April, July, and October

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Fig. 5

The relationship between daily PV energy production and relative humidity in a typical month of January, April, July, and October

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Fig. 6

Diurnal change of yearly average hourly PV energy production, global radiation on a horizontal surface, sunshine duration, and relative humidity

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Fig. 7

Variations of daily PV energy production, global radiation on horizontal surface and air temperature during the whole year from March in 2010 to February in 2011

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Fig. 8

The distribution of loading on every common factor after rotation

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Fig. 9

Flow chart of forecast calculation

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Fig. 10

The relationship between daily measured values and estimated values from March in 2010 to February in 2011 (fitting): (a) new method integrating factor analysis and multiple regression analysis and (b) traditional multiple regression method

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Fig. 11

Comparisons of the daily measured values and estimated values in typical month of (a) January, (b) April, (c) July, and (d) October (fitting)

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Fig. 12

Residual analysis in typical month of January, April, July, and October (fitting): (a) residuals and f1 and (b) residuals and f2

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Fig. 13

Comparisons of the daily measured values and estimated values for independent inspection of March in 2011 (prediction)

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Fig. 14

Statistical MAPE and rRMSE for typical months fitting and the following month prediction at monthly level from daily data

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