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Renewable Energy Trends and Applications Part 6

Tham khảo tài liệu 'renewable energy trends and applications part 6', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Smart Dispatch and Demand Forecasting for Large Grid Operations with Integrated Renewable Resources 89 4.5 Composite demand forecasting To generate better forecasting results a composite forecast is developed to mix multiple methods for STLF with CI estimation. The concept is based on the statistical model of ensemble forecasting to produce an optimal forecast by compositing forecasts from a number of different techniques. The method is depicted schematically in Figure 3. Fig. 10. Ensemble forecasting As illustrated in Figure 11 the method runs three sample models Forecast 1 Forecast 2 and Forecast 3 in parallel. The weights of the combination are theoretically derived based on the interactive multiple model approach Bar-Shalom et al 2001 . For methods which are based on Kalman filters and have dynamic covariance matrices on the forecast load these dynamic covariance matrices are used for the combination. Otherwise static covariance matrices derived from historic forecasting accuracy are used instead. CLFCC Composite load forecasting and covariance combination MPC Mixing probability calculation Fig. 11. Structure of composite forecasting with confidence interval estimation 90 Renewable Energy - Trends and Applications The relative increment RI in load is used to help capture the load features in the method since it removes a first-order trend and anchor the prediction by the latest load Shamsollahi et al. 2001 . After normalization the RI in load of last time period z t is denoted as the input to the NN where time t is the time index. The mixing weight fi t can be calculated through the likelihood functions Aj t with superscript j 1 2 3 representing Forecasts 1 2 3 respectively A j t N jz t z j t t -1 Sj t 17 Rj t A j t CịỊ 2A j t cj j _ 3 where Cj 2p Pij Ri t -1 j 1 2 3 18 where p is the transition probability to be configured manually. Si S2 and S3 are sample covariance matrices from Forecasts 1 2 3 derived from historic forecasting accuracy. Without loss of .