Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Comparison of Contemporary Protocols for High-speed Data Transport via 10 Gbps WAN Connections

This work is dedicated to comparison of open source as well as proprietary transport protocols for highspeed data transmission via IP networks. The contemporary common TCP needs significant improvement since it was developed as general-purpose transport protocol and firstly introduced four decades ago. In nowadays networks, TCP fits not all communication needs that society has. Caused of it another transport protocols have been developed and successfully used for e.g. Big Data movement. In scope of this research the following protocols have been investigated for its efficiency on 10Gbps links: UDT, RBUDP, MTP and RWTP. The protocols were tested under different impairments such as Round Trip Time up to 400 ms and packet losses up to 2%. Investigated parameters are the data rate under different conditions of the network, the CPU load by sender andreceiver during the experiments, size of feedback data, CPU usage per Gbps and the amount of feedback data per GiByte of effectively transmitted data. The best performance and fair resources consumption was observed by RWTP. From the opensource projects, the best behavior is showed by RBUDP.

Determination of sea surface salinity and wind speed by L-band microwave radiometry from a fixed platform

The European Space Agency Soil Moisture andOcean Salinity (SMOS) mission aims at obtaining global maps ofsoil moisture and sea surface salinity from space for large-scale andclimatic studies. It uses an L-band (1400–1427 MHz) MicrowaveInterferometric Radiometer by Aperture Synthesis to measurebrightness temperature of the earth’s surface at horizontal andvertical polarizations ( h and v). These two parameters will beused together to retrieve the geophysical parameters. The retrievalof salinity is a complex process that requires the knowledge ofother environmental information and an accurate processing ofthe radiometer measurements. Here, we present recent resultsobtained from several studies and field experiments that were partof the SMOS mission, and highlight the issues still to be solved.

An event-based approach to validating solar wind speed predictions: high-speed enhancements in the Wang-Sheeley-Arge model

One of the primary goals of the Center for Integrated Space Weather Modeling (CISM) effort is to assess and improve prediction of the solar wind conditions in near‐Earth space, arising from both quasi‐steady and transient structures. We compare 8 years of L1 in situ observations to predictions of the solar wind speed made by the Wang‐Sheeley‐Arge (WSA) empirical model. The mean‐square error (MSE) between the observed and model predictions is used to reach a number of useful conclusions: there is no systematic lag in the WSA predictions, the MSE is found to be highest at solar minimum and lowest during the rise to solar maximum, and the optimal lead time for 1 AU solar wind speed predictions is found to be 3 days. However, MSE is shown to frequently be an inadequate “figure of merit” for assessing solar wind speed predictions. A complementary, event‐based analysis technique is developed in which high‐speed enhancements (HSEs) are systematically selected and associated from observed and model time series. WSA model is validated using comparisons of the number of hit, missed, and false HSEs, along with the timing and speed magnitude errors between the forecasted and observed events. Morphological differences between the different HSE populations are investigated to aid interpretation of the results and improvements to the model. Finally, by defining discrete events in the time series, model predictions from above and below the ecliptic plane can be used to estimate an uncertainty in the predicted HSE arrival times.

An assessment of a three-beam Doppler lidar wind profiling method for use in urban areas

Currently there are few observations of the urban wind field at heights other than rooftop level. Remote sensing instruments such as Doppler lidars provide wind speed data at many heights, which would be useful in determining wind loadings of tall buildings, and predicting local air quality. Studies comparing remote sensing with traditional anemometers carried out in flat, homogeneous terrain often use scan patterns which take several minutes. In an urban context the flow changes quickly in space and time, so faster scans are required to ensure little change in the flow over the scan period. We compare 3993 h of wind speed data collected using a three-beam Doppler lidar wind profiling method with data from a sonic anemometer (190 m). Both instruments are located in central London, UK; a highly built-up area. Based on wind profile measurements every 2 min, the uncertainty in the hourly mean wind speed due to the sampling frequency is 0.05–0.11 m s−1. The lidar tended to overestimate the wind speed by ≈0.5 m s−1 for wind speeds below 20 m s−1. Accuracy may be improved by increasing the scanning frequency of the lidar. This method is considered suitable for use in urban areas.

Verification of European subseasonal wind speed forecasts

Analysis of the forecasts and hindcasts from the ECMWF 32-day forecast model reveals that there is statistically significant skill in predicting weekly mean wind speeds over areas of Europe at lead times of at least 14–20 days. Previous research on wind speed predictability has focused on the short- to medium-range time scales, typically finding that forecasts lose all skill by the later part of the medium-range forecast. To the authors’ knowledge, this research is the first to look beyond the medium-range time scale by taking weekly mean wind speeds, instead of averages at hourly or daily resolution, for the ECMWF monthly forecasting system. It is shown that the operational forecasts have high levels of correlation (~0.6) between the forecasts and observations over the winters of 2008–12 for some areas of Europe. Hindcasts covering 20 winters show a more modest level of correlation but are still skillful. Additional analysis examines the probabilistic skill for the United Kingdom with the application of wind power forecasting in mind. It is also shown that there is forecast “value” for end users (operating in a simple cost/loss ratio decision-making framework). End users that are sensitive to winter wind speed variability over the United Kingdom, Germany, and some other areas of Europe should therefore consider forecasts beyond the medium-range time scale as it is clear there is useful information contained within the forecast.

Development of a wind gust model to estimate gust speeds and their return periods

Spatially dense observations of gust speeds are necessary for various applications, but their availability is limited in space and time. This work presents an approach to help to overcome this problem. The main objective is the generation of synthetic wind gust velocities. With this aim, theoretical wind and gust distributions are estimated from 10 yr of hourly observations collected at 123 synoptic weather stations provided by the German Weather Service. As pre-processing, an exposure correction is applied on measurements of the mean wind velocity to reduce the influence of local urban and topographic effects. The wind gust model is built as a transfer function between distribution parameters of wind and gust velocities. The aim of this procedure is to estimate the parameters of gusts at stations where only wind speed data is available. These parameters can be used to generate synthetic gusts, which can improve the accuracy of return periods at test sites with a lack of observations. The second objective is to determine return periods much longer than the nominal length of the original time series by considering extreme value statistics. Estimates for both local maximum return periods and average return periods for single historical events are provided. The comparison of maximum and average return periods shows that even storms with short average return periods may lead to local wind gusts with return periods of several decades. Despite uncertainties caused by the short length of the observational records, the method leads to consistent results, enabling a wide range of possible applications.

Centennial changes in the solar wind speed and in the open solar flux

We use combinations of geomagnetic indices, based on both variation range and hourly means, to derive the solar wind flow speed, the interplanetary magnetic field strength at 1 AU and the total open solar flux between 1895 and the present. We analyze the effects of the regression procedure and geomagnetic indices used by adopting four analysis methods. These give a mean interplanetary magnetic field strength increase of 45.1 ± 4.5% between 1903 and 1956, associated with a 14.4 ± 0.7% rise in the solar wind speed. We use averaging timescales of 1 and 2 days to allow for the difference between the magnetic fluxes threading the coronal source surface and the heliocentric sphere at 1 AU. The largest uncertainties originate from the choice of regression procedure: the average of all eight estimates of the rise in open solar flux is 73.0 ± 5.0%, but the best procedure, giving the narrowest and most symmetric distribution of fit residuals, yields 87.3 ± 3.9%.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

Wind Power Suitability Analysis for the Gulf of Maine

This map shows one option for a viable energy source that is clean, free and endless: wind power. This map shows that the coast of Maine has the potential space and wind speed to be a location for wind farms. Four NOAA buoys placed in different locations along the Maine coast are the source of the wind speed data for this project. The average wind speed of every ten minutes of every day for the year 2004 were averaged so that each buoy was represented by one number of wind speed measured in meters/ second. The values in between these four buoys were estimated, or interpolated, using ArcGIS. Other factors that I took into consideration during this lab were distance from airports (no wind farm can be with in a three mile radius of an airport ) and distance from counties (no one wants an offshore wind farm that obstructs their view). I calculated the most appropriate locations for a wind farm in ArcGIS, by adding these three layers. The final output shows an area along Mt. Desert to be the most appropriate for development.

Evaluating the effects of high penetrations of roof-top wind Turbines on secondary distribution circuits

Incentives for using wind power and the increasing price of energy might generate in a relatively short time a scenario where low voltage customers opt to install roof-top wind turbines. This paper focuses on evaluating the effects of such situation in terms of energy consumption, loss reduction, reverse power flow and voltage profiles. Various commercially-available roof-top wind turbines are installed in two secondary distribution circuits considering real-life wind speed data and seasonal load demand. Results are presented and discussed. © 2006 IEEE.