Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

Abstract

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.

Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop

“…These data requirements are not dissimilar to those of statistical methods such as MOS. For dynamic methods such as DICast that are retrained frequently, less data may be required to produce optimal results—DICast can be optimized with 90 days of data or less [ 15 , 20 ]. The computational time for these methods is trivial in comparison with the time to accomplish the NWP simulations.…”

“…Figure 1 demonstrates the DICast post-processing methodology, which is representative of the many other systems currently being used. DICast has evolved over time to include additional machine-learning methods and has been shown to dramatically improve forecasts across multiple weather-dependent applications including road conditions [16], precision agriculture, wind and solar energy [17][18][19][20], among others. Now, many commercial weather companies and national centres employ AI-based post-processing methods [21].…”

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

“…In a related study in the context of wind energy, Phipps et al (2020) find that a two-step strategy of post-processing both wind and power ensemble forecasts performs best and that the calibration of the power predictions constitutes a crucial step. Ideally, statistical post-processing of solar irradiance forecasts could contribute an important component to modern, fully integrated renewable energy forecasting systems (see e.g., Haupt et al, 2020).…”

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

“…Some studies have shown that ML-based models outperform conventional statistical methods (i.e., time series approaches) in which the inputs are based on time series data from measurements [5][6][7][8]. It is known that, with the increase of time horizon, the inclusion of NWP inputs becomes more and more important, especially for time horizons longer than 6 h. In recent years, research works on developing NWP+ML models for wind power forecasting have emerged [9][10][11][12][13][14][15][16]. Mostly, the role of ML can be regarded as statistical downscaling, which downscale NWP results into power through a transfer function trained with historical data.…”

“…Ref. [16] describes an integrated wind and solar energy forecasting system which is developed for the Shagaya Renewable Energy Park in Kuwait by blending physical models with artificial intelligence. Testing results showed that the normalized RMSE for wind power forecasts at horizons between 12 and 24 h is about 20%.…”