Case Report
Version 1
Preserved in Portico This version is not peer-reviewed
PM-Gati Shakti: A Case Study of Demand Forecasting
Version 1
: Received: 6 August 2023 / Approved: 7 August 2023 / Online: 7 August 2023 (10:45:42 CEST)
How to cite: Reddy M, S.; G, G. PM-Gati Shakti: A Case Study of Demand Forecasting. Preprints 2023, 2023080535. https://doi.org/10.20944/preprints202308.0535.v1 Reddy M, S.; G, G. PM-Gati Shakti: A Case Study of Demand Forecasting. Preprints 2023, 2023080535. https://doi.org/10.20944/preprints202308.0535.v1
Abstract
PM-Gati-Shakti Initiative, integration of ministries, including railways, ports, waterways, logistic infrastructure, mass transport, airports, and roads. Aimed at enhancing connectivity and bolstering the competitiveness of Indian businesses, the initiative focuses on six pivotal pillars known as "Connectivity for Productivity": comprehensiveness, prioritization, optimization, synchronization, analytical, and dynamic. In this study, we explore the application of these pillars to address the problem of "Maximum Demand Forecasting in Delhi." Electricity forecasting plays a very significant role in the power grid as it is required to maintain a balance between supply and load demand at all times, to provide a quality electricity supply, for Financial planning, generation reserve, and many more. Forecasting helps not only in Production Planning but also in Scheduling like Import / Export which is very often in India and mostly required by the rural areas and North Eastern Regions of India. As Electrical Forecasting includes many factors which cannot be detected by the models out there, We use Classical Forecasting Techniques to extract the seasonal patterns from the daily data of Maximum Demand for the Union Territory Delhi. This research contributes to the power supply industry by helping to reduce the occurrence of disasters such as blackouts, power cuts, and increased tariffs imposed by regulatory commissions. The forecasting techniques can also help in reducing OD and UD of Power for different regions. We use the Data provided by a department from the Ministry of Power and use different forecast models including Seasonal forecasts for daily data.
Keywords
Machine Learning; Time-Series Forecasting; Demand Forecasting; PM Gati-Shakti; Ministry of Power; Delhi
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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