| Autor: |
B Rajanarayan Prusty, Tirthankar Chakraborty, Udith Shyamsukha, Kishore Bingi, Nimish Jain |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies. |
| Popis: |
This paper effectively devised a novel approach to characterize the predictable variations in a multi-time instant ambient temperature time series. A multiple linear regression model is used to capture the annual predictable variations accurately. The clues for predictable variations upon detailed analysis of multi-time instant daily time resolution ambient temperature data led to the invention of a set of theoretical relevant deterministic regressors forming a reducing order model. A detailed result analysis has established that the proposed model is a suitable candidate for multi-time instant daily time step data and can be extended for the risk assessment of system analysis that accounts for the temperature effect. Further, probabilistic forecasting using regression-based methods can easily combat the above-limited number of theoretical relevant regressors for decent interval forecasts. The proposed model's effectiveness is analyzed using historical ambient temperature records collected from three distinct places in India. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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