scholarly journals Weather Forecasting Based on Data-Driven and Physics Informed Reservoir Computing Models

2021 ◽  
Author(s):  
Yslam D. Mammedov ◽  
Ezutah Udoncy Olugu ◽  
Guleid A. Farah
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Weather Forecasting ◽  
Renewable Energy Sources ◽  
Weather Prediction ◽  
Prediction Method ◽  
Weather Conditions ◽  
Forecasting Analysis ◽  
Global Energy Supply ◽  
Atmospheric System

Abstract In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

scholarly journals Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3139 ◽  
Author(s):  
Félix Hernández-del-Olmo ◽  
Elena Gaudioso ◽  
Natividad Duro ◽  
Raquel Dormido
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Weather Forecasting ◽  
Wastewater Treatment Plants ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Soft Sensor ◽  
Physical Sensors ◽  
Useful Signal

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

scholarly journals A Short-Term Ensemble Wind Speed Forecasting System for Wind Power Applications

2012 ◽  
Vol 51 (10) ◽  
pp. 1763-1774 ◽  
Author(s):  
Justin J. Traiteur ◽  
David J. Callicutt ◽  
Maxwell Smith ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Statistical Models ◽  
Bayesian Model Averaging ◽  
Numerical Models ◽  
Weather Prediction ◽  
Environmental Stability ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Forecasting System

AbstractThis study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Control of BESS Using Frequency-Linked Pricing with High Wind Penetration

2014 ◽  
Vol 511-512 ◽  
pp. 1099-1102
Author(s):  
Zhen Bao Sun
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Test System ◽  
High Wind ◽  
Battery Energy ◽  
The Impact

Recently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price-based operation of BESS in a real-time market with high wind penetration using frequency-linked pricing. The authors propose a real-time market in which real-time prices are based on the grid frequency. A model for real-time price-based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real-time prices.

scholarly journals Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

scholarly journals Features of operation of wind power stations as an supplementary source of electricity for non-traction consumers of railway electric mains

2019 ◽  
pp. 36-41
Author(s):  
Kachan Yu ◽  
Kuznetsov V
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Supply ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Energy Sources ◽  
Railway Transport ◽  
Supply Systems

Purpose. Identify the features of operation of wind farms as an auxiliary supplier of electricity for non-traction consumers of railway networks and analyze the main factors that directly affect the use of wind farms due to the random nature of wind flow and additional factors due to the above conditions in different climates. The research methodology is based on modern methods of computational mathematics, statistics and information analysis using modern computer technology. Findings. The need to use renewable energy sources in the power supply systems of non-traction consumers of railway transport is obvious. Given the constant growth of prices and tariffs for electricity in Ukraine, more and more attention is paid to its savings and the search for the cheapest and most affordable alternative sources. The authors consider issues related to the possibility of using additional generation of electricity in the power supply systems of railway transport through the use of wind turbines, including for non-traction consumers. The analysis of wind flow features in some regions of Ukraine was carried out, and the measurement of wind speed in Zaporizhia and Dnipropetrovsk regions was obtained with the help of a compact wind speed sensor manufactured by Micro-Step-MIS LLC (Russia). The obtained values of wind speed were recorded and stored digitally. The received information of the above device was processed. The authors conclude that in the case of using wind turbines as an additional power source in the networks of non-traction consumers of railway power supply systems it is economically advantageous to connect them directly to these networks and fully use all electricity produced by them, reducing its consumption from this power supply system. The originality is that the use of renewable energy sources in the power supply systems of non-traction consumers of railway transport, in particular wind turbines, is proposed. Practical implications. Introduction of wind power plants as an auxiliary supplier of electricity for non-traction consumers of railway power grids in order to minimize electricity costs. Keywords: renewable energy sources, quality of electric energy, wind power plant, power supply networks of railway transport, non-traction consumers of railway electric networks, electricity production, wind speed.

scholarly journals A Remotely Deployable Wind Sonic Anemometer

2017 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Muhammad Awais ◽  
Syed Suleman Abbas Zaidi ◽  
Murk Marvi ◽  
Muhammad Khurram
Keyword(s):  
Wind Speed ◽  
Systems Engineering ◽  
Business Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Sonic Anemometer ◽  
Ground Truth ◽  
Weather Conditions ◽  
Average Wind Speed ◽  
Ground Truth Data

Communication and computing shape up base for explosion of Internet of Things (IoT) era. Humans can efficiently control the devices around their environment as per requirements because of IoT, the communication between different devices brings more flexibility in surrounding. Useful data is also gathered from some of these devices to create Big Data; where, further analysis assist in making life easier by developing good business models corresponding to user needs, enhance scientific research, formulating weather prediction or monitoring systems and contributing in other relative fields as well. Thus, in this research a remotely deployable IoT enabled Wind Sonic Anemometer has been designed and deployed to calculate average wind speed, direction, and gust. The proposed design is remotely deployable, user-friendly, power efficient and cost-effective because of opted modules i.e., ultrasonic sensors, GSM module, and solar panel. The testbed was also deployed at the roof of Computer & Information Systems Engineering (CIS) department, NED UET. Further, its calibration has been carried out by using long short-term memory (LSTM), a deep learning technique; where ground truth data has been gathered from mechanical wind speed sensor (NRG-40 H) deployed at top of Industrial & Manufacturing (IM) department of NED UET. The obtained results are satisfactory and the performance of designed sensor is also good under various weather conditions.

scholarly journals The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1984
Author(s):  
Christiaan Oosthuizen ◽  
Barend Van Wyk ◽  
Yskandar Hamam ◽  
Dawood Desai ◽  
Yasser Alayli
Keyword(s):  
Weather Forecasting ◽  
Forecast Error ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Model Output ◽  
Energy Forecasting ◽  
Weather Forecasts ◽  
Improved Performance ◽  
Model Output Statistics ◽  
Auxiliary Services

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

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