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• W4296127489 endingPage "102707" @default.
• W4296127489 startingPage "102707" @default.
• W4296127489 abstract "Long term energy requirement forecast is necessary for any policy decisions concerning management of resources and its expansions. Numerous works have been reported in the past which primarily focus on short or medium term forecasts which are not adequate for policy decision for the future. The long term forecasts requires a different approach due to the fact that the future trends on energy requirements are uncertain. This study proposes a long-term energy requirement forecast (upto 2030) using Artificial Neural Network (ANN) by correlating the relationship between the trend in the Gross Domestic Product (GDP) and energy demand, and the results are reported in a probabilistic sense by considering the best fitting probability distribution function. The proposed method is applied to the energy requirement data of five different States of India for 13 years (2008–2020). The forecast of future energy requirements and exceedance probability is estimated and discussed by considering 4 different scenarios from no change in trend to 25 % increase. Statistical assessment is done for three different probability distributions and is tested by goodness-of-fit tests namely, the Kolmogorov-Smirnov test, Anderson-Darling test and Chi-squared test to find the best probability fit. It is seen that Johnson SB distribution best fits the data of combined Andhra Pradesh and Telangana, Generalized Extreme Value distribution for the data of Karnataka, Kerala and Tamil Nadu, whereas Kumarasamy distribution best fits the data of Puducherry. In addition, based on a rank of goodness-of-fit tests, it can be concluded that 97 % minimum exceedance probability condition is maintained for overall states under four different conditions. It is concluded that the States will be able to meet the average energy requirement with minimal policy intervention for all the 10 % scenario whereas for the other scenarios a careful policy change may be required to create avenue for energy generation. However, there is only 1 % to 5 % exceedance probability for the 15 % scenario." @default.
• W4296127489 created "2022-09-17" @default.
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• W4296127489 date "2022-10-01" @default.
• W4296127489 modified "2023-09-26" @default.
• W4296127489 title "Long term monthly prediction of energy requirements from a probabilistic perspective - A case study in southern States of India" @default.
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• W4296127489 doi "https://doi.org/10.1016/j.seta.2022.102707" @default.
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