Free electricity trade market requires wind power producers to forecast day-ahead power generation. Mismatch between forecasted and real generation becomes direct losses for the wind power plant owner. However, due to intermittent nature of the wind, simple forecasting models usually are not good enough to reach satisfactory results. Automatic and self-learning algorithms are needed to ensure efficient and accurate power generation forecasting without man-hours consumption. Therefore, EA-PSM SAS provides feature to forecast hourly wind park power generation.
Mismatch between forecasted and real generation becomes direct losses for the wind power plant owner.
WHY YOU SHOULD CHOOSE EA-PSM SAS FOR WIND PARK POWER GENERATION FORECASTING?
IT WORKS TO MAXIMIZE YOUR EARNINGS
Our algorithms are designed to maximize your earnings. For this reason, we not only invest into improving wind power forecasting models accuracy but also into creating algorithms that allow to optimize overall benefit for the wind park owner. For example, additionally we do electricity load forecasting to know future demand in the market. By connecting results from the demand and generation models, we seek to create more benefits for you.
IT HELPS TO MAINTAIN WIND POWER PARK
Accurate short – term power forecasting results are automatically compared to measured power generation values. System automatically informs about unusual mismatch between measured and forecasted results. This way wind turbine technical problems can be diagnosed in advance. This allows to do automatic predictive maintenance for the wind power plant.
IT PROVIDES ONE OF THE BEST COST TO BENEFITS RATIO IN THE MARKET
Our system provides additional benefits (like predictive maintenance and earnings maximization) for one of the best price in the market. Therefore, our system can be also beneficial for small wind parks.
OPTIMIZED MODEL FOR EACH WIND POWER PARK
In order to reduce forecasting error, we adapt our forecasting models for each wind power park separately.
HIGH FLEXIBLE AND CONFIGURABLE
Minimal data required for the model is historical hourly power generation for at least several months. However, historical wind speed measurements in the wind park location, technical characteristics of the turbines and other data can be also used to get more accurate forecasting results.