Grid Load Forecasting & Optimization

Typical outcomes for this kind of engagement. Results depend on your data and starting point.

Demand volatility has risen sharply with distributed solar, electric vehicles and changing consumption patterns, while forecasting methods have not kept pace.

• Volatile demand driving systematic over-procurement of generation and capacity
• Imbalance penalties and emergency purchases when forecasts miss
• Legacy statistical methods unable to absorb weather, solar and EV signals

CloudGate builds probabilistic load forecasting on Azure Machine Learning, blending weather forecasts, calendar and seasonality effects, historical consumption and distributed generation signals into hierarchical forecasts from system down to feeder level.

• Probabilistic hierarchical forecasting from system to substation and feeder level
• Weather, calendar, solar generation and EV charging signals in the feature set
• Confidence intervals enabling reserve sizing to a stated risk appetite