In order for utilities to accurately forecast and analyze load, it’s critical to understand how changes in weather and consumer behavior impact demand.
Utilities typically utilize a sampling or “top down” approach to analyze and predict load, which has limitations and is less accurate than analyzing data from all smart meters.
Weather variability, coupled with recent trends in consumer behavior, energy efficiency, and consumer-side generation are making sampling methods less effective and increasing forecasting errors (energy generated from rooftop solar panels is not as reliable and predictable compared to traditional power sources, for example).
- Improved short-term forecasts and scenario modeling
- Reduced energy procurement costs
- Increased regulator and investor confidence
Using granular metering data, Utegration LoadPlanning4U assists in identifying trends in peak and load over a multi-year span.
Understanding the underlying causes for load variations is critical to a utility, in particular when undertaking a rate case. With Utegration LoadPlanning4U, load variation can be broken down into weather related, customer growth/decline and energy efficiency.
Data Processing and Storage
Analyzes and predicts load more effectively by utilizing the power of Hadoop, enabling the ability to analyze all smart meter data as opposed to a sampling of meter data.
Reduces the compute time and costs required to conduct scenario modeling, so more complex algorithms can be used, resulting in greater forecast accuracy.