Load Forecasting
PowerForecaster is a statistical tool, employing weather data and historical actual load data as input to generate load forecasts on a day-ahead and hourly basis and present the results to the RTOs for each service territory. LMP forecasting can also be performed using statistical analysis
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POWERFORECASTER
PowerForecaster is a statistical tool, employing weather data and historical actual load data as input to generate load forecasts on a day-ahead and hourly basis and present the results to the RTOs for each service territory. End users may choose to either use the Load Forecast created by the product, modify the Load Forecast, or enter their own Load Forecast. PowerForecaster can be run several times each morning as part of the day-ahead process and executed each hour with revised weather updates. Historical Load and Forecasted Load data resides in the databases indefinitely.
- AS Modeler creates a bottom-up Load Forecast
- Operations Modeler provides top-down Load Forecasting
- Weather Forecasts for specific cities from multiple vendors can be utilized
- Integration with over 10+ weather vendors out fo the box
FREQUENTLY ASKED QUESTION LMP LOAD FORECASTING
Weather data, such as temperature, humidity, and wind speed, has a significant impact on energy consumption patterns. Incorporating it in load forecasting improves the accuracy of predicting power demand.
Load forecasting facilitates efficient power system operation and maintenance planning, optimizes energy trading, and helps manage peak loads, all of which contribute to significant cost savings.
Load forecasting helps maintain a balance between supply and demand, optimizes grid operations, and plans maintenance schedules, thereby enhancing grid reliability.
LMP forecasting predicts electricity prices at different grid locations, considering factors like demand, transmission constraints, and generation capacity. It’s related to load forecasting as both rely on statistical analysis and share similar input factors.
Load forecasting provides valuable insights into future demand patterns, enabling better trading decisions, optimizing energy purchases, and managing price risks.
