Current status of wind energy forecasting and a hybrid method for hourly predictions

Relevance vector machine, a sparse probabilistic learning machine based on the kernel function, has excellent ability of prediction and generalization. It is proposed by this paper that the optimized relevance vector machine (ORVM) is a wind power interval forecasting model which is able to provide a certain prediction value and its possible fluctuation range at a given confidence level. The proposed model characterizes in insufficient sample training and uncertainty analysis and is greatly suitable to most of wind farms in China (newly built or large scale wind farms). First, a grouping mechanism has been used to divide wind turbines into several groups to establish forecasting model separately. Second, a selection method properly taking the characteristics of NWP error distribution into consideration was presented to improve forecasting accuracy of each group. Third, the parameters of the kernel function and initial value of iteration are determined by particle swarm optimization to further enhance forecasting accuracy. Two wind farms in China are involved in the process of primary data collection. The performance data obtained from ORVM models are tested against the predicted data generated by GA–ANN and SVM. Results show that the proposed model has better prediction accuracy, wider application scope and more efficient calculation.

Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices. 

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

 This study investigates an extreme weather event that impacted the
United Arab Emirates (UAE) in March 2016, using the Weather Research and
Forecasting (WRF) model version 371 coupled with its hydrological modeling
extension package (WRF-Hydro) Six-hourly forecasted forcing records at
05 ∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast
System (GFS), are used to drive the three nested downscaling domains of both
standalone WRF and coupled WRF–WRF-Hydro configurations for the recent
flood-triggering storm Ground and satellite observations over the UAE are
employed to validate the model results The model performance was assessed
using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 01 ∘ product), soil moisture
from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 01 ∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 025 ∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product) The Pearson correlation coefficient (PCC), relative
bias (rBIAS), and root-mean-square error (RMSE) are used as performance
measures Results show reductions of 24 % and 13 % in RMSE and rBIAS
measures, respectively, in precipitation forecasts from the coupled
WRF–WRF-Hydro model configuration, when compared to standalone WRF The
coupled system also shows improvements in global radiation forecasts, with
reductions of 45 % and 12 % for RMSE and rBIAS, respectively
Moreover, WRF-Hydro was able to simulate the spatial distribution of soil
moisture reasonably well across the study domain when compared to AMSR2-derived soil
moisture estimates, despite a noticeable dry and wet bias in areas where soil
moisture is high and low Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which
indicates the model's capability to simulate surface drainage Finally, the
coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF
simulation, which is attributed to the effect of soil moisture feedback The
demonstrated improvement, at the local scale, implies that WRF-Hydro coupling
may enhance hydrological and meteorological forecasts in hyper-arid
environments

/pdf/analysis-of-an-extreme-weather-event-in-a-hyper-arid-region-ficfrhdibb.pdf

Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data

Accurate wind power forecast is very important in order to construct smart electric grids. Nevertheless, this task still constitutes a challenge because wind is a very variable and local phenomenon. It is difficult to downscale information coming from Numerical Weather Prediction (NWP) models down to wind farm level and this is especially true onshore, in complex terrain conditions. Artificial Intelligence often comes at hand, for its power in learning what is hidden inside data: Artificial Neural Networks (ANN) are therefore commonly employed for wind power forecast. In this work, a pure ANN method is compared against a hybrid method, based on the combination of ANN and a numerical method based on physically-consistent assumptions (Computational Fluid Dynamics). Both approaches are validated against the SCADA data of a wind farm sited in Italy in a very complex terrain. It arises that the two methods have overall similar performances on average. However, pure ANN turns out to forecast better at mid-energy levels and during cut-off events at the highest wind speed, whereas the hybrid method forecasts better during low and high wind speed ranges. This makes the two approaches complementary and promising for future applications through an ensemble strategy.

https://www.ijrer.com/index.php/ijrer/article/download/6186/pdf

Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site

Wind energy is a rapidly growing industry in Turkey Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey Wind speed forecasting is necessary for power systems because of the intermittent nature of wind Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead The wind speeds and wind directions are evaluated based on the mean absolute error (MAE) Evaluations were also performed seasonally Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction The model is also able to reproduce wind directions except for low wind speeds Large MAEs are found for the winds less than 5 m/s The performance of the WRF model for wind power prediction decreases with the increasing runtime Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant

Short term wind energy resource prediction using WRF model for a location in western part of Turkey

Wind power forecasting has recently become important to fulfill the increasing demand on energy usage. Two main approaches are applied to the wind power forecasting which can vary from 6 hours to 48 hours. One way is to model the atmosphere dynamically and the other method is to analyze wind speed and direction statistically. Although dynamical models forecast better than statistical models, since the former solves the problem physically, statistical models can be preferable when short term forecasting is needed due to their quick response time. Most of the currently available wind power forecasting systems analyzes the effect of wind field on wind power based on numerical weather prediction models. However, the resolution of these models is not sufficient enough when the scale of the turbines on the wind farms is considered. It is possible to overcome this problem by using computational fluid dynamics (CFD) models, which can provide both linear and nonlinear solutions on the turbine scale in terms of both wind speed and wind power forecasting. In this study, the WRF model is used for 72-hour wind speed and direction forecasting. The initial and boundary conditions of the model are provided by ECMWF operational forecasting data with the resolution of 0.25 degree. The WRF model is downscaled to 1 km resolution over Manisa Soma wind farm and 72-hour forecasts for each day of 2010 are accomplished. WindSim uses wind speed and direction values, which are solved on the nearest grid point of the WRF model to the location of a wind turbine, to simulate high-resolution wind speed values for 72hours. These WRF to WindSim coupled model results are compared to the wind power observations. As a result, we found that daily wind power generation errors per turbine vary between 90kW and 200kW for the seasons of spring, summer, and fall, whereas the error is about 150–350kW for winter. We also compared the errors of 24 hourly model outputs and we found that there is no significant difference among the first, the second, and the third 24 hourly forecasts. We finally applied model output statistics to the WRF to WindSim coupled model results in order to minimize their errors.

72hr forecast of wind power in Mani̇sa, Turkey by using the WRF model coupled to WindSim

Modeling the Colorado Front Range Flood of 2013 with Coupled WRF and WRF-Hydro System

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Short term wind energy resource prediction using WRF model for a location in western part of Turkey

72hr forecast of wind power in Mani̇sa, Turkey by using the WRF model coupled to WindSim

Modeling the Colorado Front Range Flood of 2013 with Coupled WRF and WRF-Hydro System