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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Prediction of solar energy forecasting by using linear and logistic regression : A review and geographical comparative analysis in Indian context

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pp. 1185–1198Vol. 27Issue 6September 2024DOI: 10.47974/JSMS-1230XML
Received:
12 May 2020
Published Online:
19 Sep 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1230
Pages:
1185–1198

Abstract

Power generation from renewable resources is now-a-days recognized as an necessary basic skill to improve the operation of power system. Forecasting of the renewable energy is the important factor. This study on renewable energy forecasting Techniques gives the more knowledge about how the renewable energy is forecasted and how the renewable energy is converted to the electrical power. And it tells various techniques and methods for the forecasting of renewable energy resources. India is geographically diversified due to its different climatic conditions and we have collected data from solar energy sources from 6 different locations. It comprises the data collected over a period of around one year solar plant output, solar irradiations and temperature of the PV plant. The datasets are considered for training to supervised learning algorithms with linear and logistic regressions by considering short term forecasting and predict immediate next day by using last 28 days data.

Keywords

Subject Classifications

62Pxx : Application of Statistics

References

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