Electric Load Prediction

Electric Load Prediction

Accurate electric load forecasts are critical to utilities’ day-to-day operations. Load forecasting helps an electric utility make important decisions, including those on purchasing and generating electric power and load switching. Not only are load forecasts extremely important for energy suppliers, they’re important for many other participants in electric energy generation, transmission, distribution, and markets.

Balancing the electrical grid occurs on a variety of time scales, ranging from short term (about one hour to a week) for day-to-day operations, medium range (about one week to a year) for maintenance and longer planning, and long term (beyond a year) for long-term planning. Current and future weather conditions, including temperature, humidity, precipitation and wind speed, must be factored in the electric load equation.

NCAR’s Load Forecasting System

NCAR, in partnership with Xcel Energy, has developed a forecast system that leverages weather observations and forecasts along with historical load data to provide short-range net electrical load forecasts.

Inputs to the load forecast module include meteorological variables from the global Meteorological Aviation Report (METAR) network and NCAR’s DICast® forecasts, time variables (hour of day, day of week, time of year), as well as solar angles, and previous load observations. The meteorological variables are comprehensive; they include temperature, dew point, wind speed, cloud cover percentage, hourly precipitation amount, probability of precipitation, and conditional probabilities of rain, ice and snow.

NCAR’s Solar Forecasting System

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this renewable resource. NCAR and a team of researchers from public, private, and academic sectors partnered to develop and assess a new, cutting-edge solar power forecasting system called Sun4Cast™. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.

Sun4Cast™ integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is NCAR’s WRF-SolarTM, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple numerical weather prediction (NWP) models are blended via the DICast® System.  For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast® systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making.

After testing Sun4Cast™ at multiple sites, the research team has determined that it can be up to 50 percent more accurate than current solar power forecasts. This improved accuracy will enable utilities to deploy solar energy more reliably and inexpensively, reducing the need to purchase energy on the spot market. 

NCAR’s Wind Power Forecasting System

Xcel Energy is employing wind power forecasting to effectively integrate wind power into its operations. Because forecast errors are real costs to the energy market, more accurate wind forecasts can save utilities and ratepayers substantial amounts of money. They will depend on accurate forecasts even more as wind power capacity increases over time.

In collaboration with Xcel Energy, NCAR has developed and upgraded its Wind Power Forecasting System that integrates high resolution and NWP modeling capabilities using NCAR’s DICast® forecasting system and its WRF model and the Real-time Four-dimensional Data-assimilation and Forecasting (RTFDDA) system. Extreme events, specifically changes in wind power due to high winds and icing, are incorporated in the wind power forecast.

This wind forecasting system ingests external, weather observations and model data. In order to provide information specific to Xcel Energy’s region, high resolution NWP simulations assimilate specific local weather observations. The weather observations range from routine meteorological surface and upper air observation systems and wind farms. The wind farm data assimilated includes wind speed data from the Nacelle anemometers. This customized model output is blended with the model output and optimized with the DICast® System. To improve estimates of short-term changes in wind power requires nowcasting technologies such as the Variational Doppler Radar Analysis System (VDRAS) that is blended with an observation-based expert system.