New Guideline Outlines Three-Stage Roadmap For Smarter Power Demand Forecasting By Indian Discoms – Report

A new report released by Prayas (Energy Group) in collaboration with the All India Discoms Association has presented a practical roadmap to help Indian electricity distribution companies improve the way they forecast electricity demand. Published in June 2026, the guideline comes at a time when the country’s power sector is experiencing rapid changes driven by rising electricity consumption and the growing adoption of clean energy technologies.

According to the report, traditional methods of forecasting electricity demand are no longer adequate. Most distribution companies have relied on extending historical electricity sales trends or estimating a single annual peak demand figure. While these approaches were useful in the past, they fail to reflect changing consumption patterns that now vary by season, time of day, weather conditions, and consumer behaviour. This often results in inefficient power procurement, higher costs, and localized stress on the electricity network.

The report highlights that several factors are reshaping electricity demand across India. These include increasing use of air conditioners due to rising temperatures, rapid urbanization, the growing number of electric vehicles, and the expansion of rooftop solar installations. Together, these developments are creating more complex demand patterns that require advanced forecasting methods.

To address these challenges, the report recommends a three-stage forecasting framework that utilities can gradually adopt using data they already collect.

The first stage focuses on forecasting demand at the overall distribution company level. Instead of relying only on annual demand estimates, utilities are encouraged to study historical load curves and examine how weather conditions influence electricity consumption. This approach provides a clearer understanding of daily and seasonal demand variations, helping utilities prepare for changing load patterns.

The second stage introduces forecasting at the feeder and geographic level. By analysing individual feeders with similar characteristics and using Geographic Information System (GIS) mapping, utilities can identify specific locations where electricity demand is increasing rapidly. These may include expanding urban areas or new industrial zones. Such spatial forecasting enables distribution companies to prioritize network upgrades, strengthen infrastructure, and plan investments more efficiently.

The third stage moves to consumer-level forecasting. The report recommends making greater use of smart meter data and targeted load research surveys to understand how appliance ownership, energy efficiency, and customer behaviour affect electricity consumption. It also highlights the use of advanced bottom-up modelling tools such as the open-source PIER energy model, which can simulate future demand scenarios for applications including electric cooking and electric vehicle charging.

The report notes that discussions with distribution companies revealed that although utilities mainly focus on short-term operational planning, they already possess much of the data needed for better long-term forecasting. However, this information often remains underutilized.

By combining system-level forecasting with local network analysis and consumer-level insights, the proposed framework offers a more comprehensive approach to demand planning. The report concludes that adopting this data-driven methodology can help distribution companies improve long-term planning, reduce costly power procurement decisions, manage emerging demand uncertainties, and develop a more reliable, efficient, and consumer-focused electricity distribution system.