Predict+ Maintains Forecast Accuracy During Major Energy System Disruptions

Executive Summary

During periods of major disruption in Israel’s energy system, Tigo Energy’s Predict+ platform helped energy producers maintain accurate electricity demand forecasts despite sudden shifts in consumption behavior.

When the conflict caused dramatic changes in electricity usage patterns across the country, Predict+ models were rapidly recalibrated to reflect the new operating environment. Within days, forecast accuracy returned close to pre-disruption levels, allowing energy operators to continue planning and market participation with confidence.

The Challenge

Energy forecasting models are typically trained on historical consumption patterns. However, during periods of extreme disruption—such as geopolitical events or large-scale behavioral shifts—these patterns can change dramatically.

In Israel’s electricity market, the conflict created sudden changes in:

• Residential electricity demand
• Commercial energy consumption
• Daily usage patterns
• Operational planning assumptions

These changes disrupted existing forecasting models and made accurate demand predictions more difficult.

Energy operators needed a system capable of adapting quickly to new conditions.

The Solution

Predict+ uses machine learning models that continuously learn from incoming data streams. When consumption patterns changed abruptly, Predict+ engineers recalibrated the forecasting models to incorporate new behavioral signals.

The platform analyzed updated inputs including:

• Smart meter consumption data
• Real-time demand patterns
• Weather signals
• Historical energy usage trends

By retraining models on the new data environment, Predict+ was able to quickly adapt forecasts to the changed consumption behavior.

Implementation

Predict+ teams monitored demand patterns and adjusted forecasting models to reflect the new reality of energy consumption.

The platform’s adaptive learning architecture allowed updated models to be deployed quickly, enabling forecasting accuracy to recover within days.

This rapid adjustment ensured that energy producers could maintain operational planning and market participation despite the disruption.

Results

Predict+ demonstrated strong resilience under extreme conditions.

Key outcomes included:

Rapid Model Adaptation

Forecasting models were recalibrated within days to reflect new consumption patterns.

Restored Forecast Accuracy

Accuracy returned close to pre-disruption levels despite major behavioral shifts.

Operational Continuity

Energy producers were able to maintain forecasting and planning capabilities during a highly volatile period.

Why This Matters

Modern energy systems are increasingly exposed to unexpected disruptions—from extreme weather events to geopolitical instability.

Predict+ is designed to adapt to these changing conditions by continuously retraining forecasting models as new data becomes available.

This capability allows energy operators to maintain accurate forecasts even when traditional models fail.