Client Overview
The client is a member-owned electric cooperative in the Upper Midwest, serving homes, farms, and small businesses across rural and suburban communities. It purchases most of its electricity through wholesale markets and supply contracts, making accurate demand forecasts essential to controlling power costs. Cold winters, growing wind and rooftop solar capacity, and the rising use of electric heating and electric vehicles made demand harder to predict.
As these factors added complexity to daily operations and long-term planning, the cooperative needed an AI-powered load-forecasting solution that could predict energy demand more accurately, identify peak hours in advance, and support better power-purchasing decisions.
Project Summary: Every power purchase starts with a demand forecast, and small errors can drive up costs. We developed an AI-powered load forecasting platform that predicts energy demand from the next hour to several years ahead using weather, customer behavior, and local generation data. The platform explains the main factors influencing each forecast and lets planners compare possible demand scenarios. The challenge was to improve accuracy during extreme weather while keeping forecasts transparent and easy to use.
Project Challenges
The cooperative relied on historical demand patterns, seasonal averages, and manual spreadsheet adjustments. These methods worked under normal conditions but struggled during sudden cold snaps, storms, and holidays. Forecasting errors led to costly peak-period power purchases and losses when surplus power had to be sold at lower prices.
Growing rooftop solar adoption, heat pumps, electric heating, and EV charging added further complexity. Solar reduced daytime grid demand, while electrification created new morning and evening peaks. Without direct visibility into these resources, planners struggled to separate weather effects from changes in customer behavior, limiting forecast accuracy.
Peak demand charges added financial pressure. Misjudging demand during a few critical hours could increase wholesale power and transmission costs, making reliable short-term load forecasting essential. However, scattered smart meter readings, SCADA data, weather forecasts, outage records, and market prices forced planners to spend hours combining information before preparing forecasts. Differences between weather forecasts made it harder to select reliable model inputs.
Long-term planning faced similar challenges. Existing models relied on simple growth rates and overlooked EV adoption, new housing, renewable generation, and changing business needs. The cooperative needed AI-powered load forecasting that could support both short-term decisions and long-term capacity planning while keeping predictions explainable, protecting customer data, and fitting into existing systems. These requirements also called for secure integration with legacy infrastructure without disrupting daily operations. The solution needed clear governance and controls to support responsible, reliable AI adoption.
Solution and Impact
We began by working with the cooperative’s planning, trading, engineering, and IT security teams to define forecasting goals. These included reducing prediction errors, identifying peak hours earlier, and cutting daily preparation time.
We built a unified data layer that automatically collects smart meter readings, SCADA data, historical demand, outage records, market prices, calendar events, and weather forecasts. We clean and align the data by time and location before it enters the forecasting models. Automated quality checks identify missing or faulty data, while aggregation and access controls protect customer information.
For short-term load forecasting, we trained an ensemble of gradient boosting and deep learning models to predict hourly demand at the system and substation levels. The models account for temperature, wind chill, humidity, holidays, and daily usage patterns. A behind-the-meter estimation module accounts for rooftop solar generation, EV charging, and electric heating to provide a clearer picture of actual grid demand. This helps planners separate weather-related changes from shifts in energy consumption.
For long-term planning, we developed a model that projects electricity demand over a one- to ten-year horizon. Planners can compare low, expected, and high demand scenarios based on population growth, electrification, local business activity, and climate trends. We also built a peak prediction engine that estimates the likelihood of each hour becoming a monthly or annual demand peak. Forecasts include confidence ranges and key contributing factors, helping planners understand predictions and make informed purchasing decisions.
A planning dashboard brings hourly forecasts, peak alerts, scenario tools, and accuracy reports into one place. Before deployment, we ran the models alongside the cooperative’s existing forecasting method, covering both winter and summer conditions. The team compared forecasts with actual demand, reviewed significant errors, and refined the models before rollout.
Our custom AI development approach provides a flexible foundation for expanding forecasting capabilities as the cooperative’s energy needs evolve.t.
Technologies We Used
Results
Following parallel testing against the cooperative’s existing forecasting method, the platform was rolled out in phases. The evaluation showed improved forecast accuracy, better anticipation of peak demand, and less time spent preparing daily forecasts.
Key Outcomes
- 38% reduction in day-ahead forecast error (MAPE), from 4.8% to under 3%.
- 44% reduction in forecast error during extreme cold events.
- Daily forecast preparation time reduced from about 3 hours to under 15 minutes.
- 60% reduction in time spent on manual data work and adjustments.
- Estimated $2.6 million in annual savings on power purchasing and peak-related costs.
Words of Appreciation
“We partnered with WPWeb Infotech to build an AI-powered load forecasting platform that has transformed how we plan our daily power purchases. Their team helped us move from manual forecasting to reliable, data-driven predictions, making it easier to anticipate peak demand and prepare for extreme weather.”
Future Scalability
The modular design gives the cooperative a flexible foundation to expand its AI-powered load forecasting capabilities as energy needs evolve. Future enhancements can include individual solar and wind site forecasts, EV charging demand prediction, and battery dispatch planning without rebuilding the platform. The models will be retrained regularly using new data and actual outcomes, and each update will be evaluated against the existing model before release.
As the cooperative adds customers, distributed energy resources, and new data sources, the cloud-based platform can scale accordingly. It can also support broader applications, including energy trading, grid investment planning, and carbon reporting. Organizations can hire AI developers to extend these capabilities, integrate new data sources, and maintain reliable forecasting as operational requirements grow.
