Ecotere provides a data‑driven platform that uses machine‑learning on large historical consumption data to generate dynamic, time‑series demand forecasts for distribution grids. The platform runs load simulations to identify causal bottlenecks and automatically recommends optimal reinforcement interventions with detailed cost‑benefit analysis, helping utilities and grid planners reduce investment risk and prioritize cost‑effective upgrades.
Funding
Funding not disclosed
Founders
Product
Problem
Utilities and distribution network operators face uncertainty when planning long‑term grid capacity upgrades because demand forecasts are often static and bottleneck analyses lack causal insight, leading to costly over‑building or insufficient reinforcement.
Solution
Ecotere offers a data‑driven platform that leverages big historical datasets and machine‑learning models to produce dynamic, time‑series demand forecasts for distribution grids. The system runs load simulations to pinpoint causal bottlenecks and then generates detailed recommendations for interventions, specifying type, size, location, and associated costs. For each proposed solution, Ecotere calculates comprehensive cost‑benefit metrics that capture grid performance, market impacts, and broader societal advantages. By integrating these analytics into a single workflow, the platform helps planners evaluate alternatives, reduce investment risk, and prioritize flexible, cost‑effective reinforcement strategies.
Target Audience
Primary customers are electric utilities, distribution system operators, and grid planning consultants responsible for long‑term capacity investment decisions.
Features
- Machine‑learning based demand forecasting using large‑scale historical consumption data
- Dynamic load simulation engine that identifies causal bottlenecks in the distribution network
- Automated recommendation engine suggesting optimal intervention type, dimension, and placement
- Integrated cost‑benefit analysis covering grid efficiency, market value, and societal benefits
- Scenario comparison tools that allow users to evaluate multiple reinforcement options side by side
- Exportable reports and data feeds compatible with common utility planning software