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Ethanol is recognised as a fuel in India due to its potential to help the nation’s ecology and energy security. In order to reap the rewards of ethanol, an Ethanol Blending Programme has been initiated. Currently, India intends to begin using gasoline with 20% ethanol by 2025 and to complete the switch by 2030. India has, however, continually fallen short of the blending goal. Moreover, this goal is still unattainable due to a lack of ethanol supply. Therefore, improvement of this scenario requires a precise ethanol demand projection for appropriate policy formulation. Besides, identifying an accurate forecasting model is also necessary to produce accurate demand projection consistently. This study attempts to identify the most suitable model among autoregressive integrated moving average, grey model and long short-term memory (LSTM) and reveals that LSTM model produces forecast more accurately with more consistency. Thus, this model is used for projecting the ethanol demand till 2030. The forecasted demand for ethanol to achieve a 20% mix will be 10 474.81 million litres through 2030. Therefore, India is likely to face continuous ethanol shortages in order to fulfil future blending requirements if the demand–supply imbalance is not mitigated.

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