Forecasting electricity demand in the data-poor Indian context

• Main Author: Alsup, Meia(Meia L.)
• Other Authors: Robert Stoner.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2021
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/129084

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 === Cataloged from student-submitted PDF of thesis. === Includes bibliographical references (pages 51-53). === Electricity demand at the grid level is steadily growing in India. More areas are getting interconnected to the grid; and with rising incomes, electricity is highly affected by adoption of air conditioning systems and electric vehicles. Compared with the developed world context where electricity demand is approximately flat if not decreasing year to year, demand in India is growing. In this paper, we aim to examine forecasting methods and determine an optimal method for forecasts in India. Despite limited historical data, we improve forecasts of electricity demand in India out to the year 2050. The forecasts are in five year increments across three different GDP growth scenarios (not accounting for Covid-19). In addition, a layer of natural variation is added to the forecasts for the purpose of modeling the role of various energy technologies on the grid. The methodology to generate more realistic sample loads from predicted average scenarios is a key contribution. === by Meia Alsup. === M. Eng. === M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science