Uncertainties in energy and weather forecasting: environmental challenges of offshore wind energy and earth observation interference by 5g millimeter wave networks
Creators: name: Golparvar, Seyyed Behzad
Publisher: name: No Publisher Supplied
Year: 2024

description: To address the uncertainties in environmental forecasting, leveraging data-driven approaches in combination with machine learning models and physics-based simulations offers a promising solution. These integrated methodologies can tackle the complex challenges posed by climate change and anthropogenic activities, thereby enhancing the accuracy and reliability of environmental predictions. This dissertation examines two critical environmental forecasting challenges associated with emerging technologies to better understand and mitigate these uncertainties, ultimately improving forecasting capabilities. The first part of the research focuses on overcoming limitations in predicting offshore wind turbine power generation by employing data-driven techniques, physics-based simulations, and machine learning. The second part explores the potential impact of the commercial deployment of millimeter-wave spectrum in fifth-generation cellular networks (5G mmWave) on the accuracy of weather satellite measurements and subsequent weather forecasting by performing 5G interference geospatial aggregation analysis along with operational scale weather forecast simulations.In the wind energy industry, power curves are used to predict electrical power for planning and operational purposes. These curves show the electrical output a wind turbine can produce at various wind speeds. However, existing power curve methods, which mostly rely on wind speed as the sole input, have limitations. They overlook the complex marine environment where offshore turbines operate and provide little information about power fluctuations around the mean value, crucial for ensuring reliable grid integration. This study investigates the impact of several wind- and wave-related factors on offshore wind power variability, aiming to accurately predict output power characteristics in terms of mean and standard deviation. The approach involves conducting stochastic simulations using OpenFAST, a multi-physics offshore wind turbine simulation tool, and Gaussian Processes as a machine learning model to reveal the underlying relationships in weather-to-power conversion. The results indicated that a multi-variate machine learning model with variables including wind speed, yaw misalignment and air density achieves the best performance with about 88% lower prediction errors compared to the standard method of bins. It was shown that wave-related factors were not explanatory variables for mean output power. Also, for output power fluctuations in terms of power standard deviation the best predictive model was a Gaussian Processes model with three variables including wind speed, significant wave height, and wave direction. Finally, testing the developed models on real-world data collected at the New York/New Jersey bight demonstrated the superiority of the proposed data-driven method over existing power curve methods. The case study results showed that the best Gaussian Processes model for mean output power was 91% more accurate than the method of bins over a 7-day period of predictions.The second study in this thesis addresses concerns raised by the remote sensing and weather forecast communities regarding the potential impacts of the 5G mmWave network, specifically the n258 band, on weather forecasting accuracy. This concern stems from the proximity of this 5G mmWave band to the 23.8 GHz frequency, a crucial frequency for measuring atmospheric water vapor by weather satellites' passive sensors. A comprehensive theoretical and numerical framework was developed to evaluate how 5G deployment might affect satellite-based weather observations. By modeling the spatio-temporal distribution of 5G base stations and calculating interference power, the research assesses the implications for weather forecast simulations. The results indicated that if interference power at the radiometer from a single base station is at a level of -175 dBW for a network of base stations with a spectral efficiency of 15 bit/s/Hz/BS, the aggregate interference power has limited impact in the year 2025 but can result in an induced noise in brightness temperature of up to 17 K in the year 2040. Furthermore, simulations using WRF and WRFDA for Super Tuesday Tornado Outbreak case revealed that such level of RFI can significantly impact the 12-hour forecast the weather event with forecasting error of up to 10 mm in precipitation. The results also showed that if the level of interference power received by the radiometer from a single base station is -200 dBW, then there is no impact on forecasting errors even in 2040. Regarding the Hurricane Ida case simulated using JEDI and UFS, results showed that that for the same abovementioned 5G mmWave RFI level, the precipitation forecast change can be up to 9.75 mm. It was also shown that RFI can affect the forecasts of other weather parameters as well such as temperature, relative humidity, precipitable water, precipitation rate, and storm surface runoff. For example, 2-meter temperature predictions can be changed by up to 2.51˚C.