Orthogonal Regression Analyses of solar Irradiation and DC Power Data
Surendra H H1, Seshachalam D2, Sudhindra K R3
1Mr. Surendra H.H.*, Assistant Professor, Department of Electronics and Communication, BMS College of Engineering, Bangalore, India.
2Mr. Seshachalam D, Professor, Department of Electronics and Communication, BMS College of Engineering, Bangalore, India.
3Mr. Sudhindra K.R., Associate Professor in the Department of Electronics and Communication, BMS College of Engineering, Bangalore, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 175-180 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1438029420/2020©BEIESP | DOI: 10.35940/ijitee.D1438.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rapid deployment of solar photovoltaic cells in recent times is enabling data analysis of renewable energy solar source and its conversion to electrical energy. The need for estimating the capacity of Solar irradiation and solar DC power in any geographical location is necessary for short-term and long term forecasting. In this paper a regression-based data analysis is carried out at different locations in India and analyze the residue for better fit. Here solar DC power is a dependent output variable and solar irradiance is an independent predictor. The orthogonal regression is a standard linear regression technique involves in reducing the sum of squared orthogonal projections. The primary goal of this paper is to analyze orthogonal regression technique with the relationship between response (DC power data) and predictor (solar irradiation) across different diverse locations. The fitted value for the solar irradiance accounts for the uncertainty in the value of the predictor. The study of nonlinear nature of solar irradiation and DC power generated across geographically dispersed sites for a time series data leads to better estimate of solar power.
Keywords: Irradiance, Orthogonal Regression, Least Square, Error Variance Ratio, Residual
Scope of the Article: Regression and prediction