Tigo Energy Expands AI-Driven Forecasting Solutions as YASNO Deploys Predict+ Across Ukraine
Tigo Energy, Inc. (NASDAQ: TYGO), a global provider of intelligent solar and energy software solutions, has announced that Ukrainian energy company YASNO has become the latest enterprise-level customer to adopt the Tigo Predict+ forecasting platform. The deployment marks another significant milestone for Tigo as utilities increasingly turn to artificial intelligence and advanced analytics to improve grid management, enhance forecasting accuracy, and optimize energy operations in increasingly complex environments.
The Predict+ platform is designed to help utilities and energy providers address one of the most pressing challenges facing modern electricity systems: accurately forecasting energy demand and generation while balancing traditional baseload resources with growing levels of renewable energy. By leveraging advanced neural-network technology and AI-powered analytics, Predict+ enables utilities to improve operational planning, reduce imbalance costs, and strengthen grid reliability.
The adoption of Predict+ by YASNO is particularly notable because of the unique operating conditions faced by the Ukrainian utility sector. As one of Ukraine’s leading providers of electricity, natural gas, and energy-efficiency services, YASNO serves more than 2.5 million residential customers across the Kyiv, Dnipropetrovsk, and Donetsk regions. In addition, the company supports more than 64,000 commercial and industrial customers, making accurate forecasting a critical component of its daily operations.
Unlike many utilities operating under relatively stable conditions, YASNO must manage a grid environment characterized by highly variable weather patterns, rapidly changing demand profiles, and ongoing infrastructure challenges. These factors make precise forecasting significantly more difficult, requiring advanced analytical tools capable of adapting to constantly evolving conditions.
The deployment of Predict+ follows the successful completion of a pilot program conducted in the Dnipropetrovsk region. The area was selected because it presents one of the most demanding forecasting environments in Ukraine due to its diverse customer base and highly dynamic weather conditions. During the pilot phase, Tigo worked closely with YASNO to configure the platform, integrate local operational data, and validate forecasting performance under real-world conditions.
Following the success of the pilot, YASNO has begun preparations for broader implementation of Predict+ across its operations. This next phase includes additional system configuration, integration of historical operational and customer data from multiple YASNO business units, and the expansion of forecasting capabilities to support a wider range of energy management functions.
According to YASNO, the platform is expected to deliver meaningful operational and financial benefits. Improved forecasting accuracy enables the company to better anticipate hourly electricity demand, optimize procurement decisions, and reduce the costs associated with energy market imbalances.
Olena Senkina, Head of Electricity Department at YASNO, highlighted the importance of the technology in improving operational performance and customer service.
She noted that the broader deployment of Predict+ will further enhance the company’s ability to forecast electricity demand on an hourly basis while reducing imbalance settlement expenses. These improvements contribute to more effective planning, greater efficiency in resource utilization, and enhanced service reliability for customers throughout the utility’s service territories.
At the core of Predict+ is an AI-driven forecasting engine that utilizes neural-network technology to analyze vast amounts of data from multiple sources. When smart meters are available, the platform can model and evaluate each meter individually, creating highly detailed consumption profiles that improve forecasting precision. By incorporating actual customer usage data, historical trends, and statistical averages, the system develops a comprehensive understanding of consumption behavior across the network.
Even in areas where smart meter penetration remains limited, Predict+ is capable of generating highly accurate forecasts using alternative data sources and advanced modeling techniques. This flexibility makes the platform suitable for utilities operating in diverse regulatory and technological environments.
The platform’s capabilities extend far beyond basic demand forecasting. Predict+ includes a broad range of analytical tools covering market intelligence, customer insights, profitability analysis, regulatory reporting support, and real-time integration with electricity spot market pricing data. These functions provide utilities with a more comprehensive view of their operations and help support informed decision-making across multiple departments.
To generate forecasts, Predict+ continuously analyzes numerous streams of information simultaneously. These include weather forecasts obtained from multiple providers, real-time meteorological conditions across various regions, historical customer consumption data, distributed generation output, and operational records from previous days. By combining these datasets, the platform can estimate how much electricity is likely to be consumed or generated during every hour of the day.
One of the system’s most powerful features is its ability to create multiple forecasting scenarios that are continuously updated as conditions evolve. As weather variables such as temperature, wind speed, cloud cover, and solar irradiation change, the platform automatically recalculates expected outcomes and adjusts forecasts accordingly.
This dynamic forecasting process allows utilities to respond more effectively to changing conditions. The system continually measures deviations between forecasted and actual results, using these insights to refine future predictions. Over time, this continuous learning process enables the platform to achieve increasingly higher levels of accuracy.
Currently, the Predict+ platform manages more than 650 gigawatt-hours of energy and delivers forecast accuracy rates of approximately 97.5% for utility customers. Such performance levels can have a substantial impact on utility operations, helping organizations reduce costs, improve market participation, and enhance overall grid stability.
For YASNO, the implementation of Predict+ carries additional significance because the utility operates in an environment where infrastructure disruptions can create sudden and unpredictable changes in energy flows. Traditional forecasting systems often struggle to account for these extraordinary circumstances, requiring more adaptive and intelligent approaches.
Archie Roboostoff, Vice President of Software at Tigo, explained that while many Predict+ forecasting models are built using data from relatively stable grid environments, the collaboration with YASNO required the company to rapidly incorporate additional variables related to disruptions affecting critical grid infrastructure.
According to Roboostoff, the flexibility of the Predict+ neural-network architecture and data framework enables the platform to quickly ingest large quantities of new information, including live operational data streams and extensive historical datasets. This capability allows the system to adapt its forecasting models to account for unusual events and changing operating conditions.
He emphasized that the ability to continuously integrate new information and transform uncertainty into more predictable outcomes represents one of the platform’s greatest strengths. The successful deployment at YASNO demonstrates how advanced AI-driven forecasting technologies can support utilities operating under some of the most challenging conditions in the world.
As electricity systems continue to evolve and renewable energy penetration increases globally, accurate forecasting is becoming an increasingly critical component of grid management. The partnership between Tigo Energy and YASNO illustrates how artificial intelligence, machine learning, and advanced analytics can help utilities improve resilience, optimize operations, and deliver more reliable energy services to customers even in highly complex and rapidly changing environments.
Source Link: https://www.businesswire.com/
