DOCSLIB.ORG

Short-Term Wind Power Forecasting Using Gaussian Processes

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Short-Term Wind Power Forecasting Using Gaussian Processes Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Short-Term Wind Power Forecasting Using Gaussian Processes Niya Chen, Zheng Qian, Xiaofeng Meng Ian T. Nabney Beihang University Aston University China United Kingdom {pekiny, qianzheng, mengxf}@aspe.buaa.edu.cn [email protected] Abstract data to build statistical models, such as autoregressive inte- grated moving average (ARIMA) [Liu et al., 2011], Kalman Since wind has an intrinsically complex and filters [Louka et al., 2008], artificial neural networks [Li stochastic nature, accurate wind power forecasts and Shi, 2010; Hong et al., 2010] and support vector ma- are necessary for the safety and economics of wind chines [Salcedo-Sanz et al., 2011]. Since wind varies rapidly energy utilization. In this paper, we investigate a with time, the statistical models are effective only for very combination of numeric and probabilistic models: short-term forecasts (about 1–4 hours ahead). On the other one-day-ahead wind power forecasts were made hand, physical models have advantages over longer horizons with Gaussian Processes (GPs) applied to the out- (from several hours to dozens of hours), because they include puts of a Numerical Weather Prediction (NWP) (3D) spatial and temporal factors in a full fluid-dynamics model. Firstly the wind speed data from NWP model. However, this type of model has limitations, such was corrected by a GP. Then, as there is always as the limited observation set for calibration, the relatively a defined limit on power generated in a wind tur- limited spatial resolution possible over such a wide area, and bine due the turbine controlling strategy, a Cen- the difficulty of accounting for local topography [Soman et sored GP was used to model the relationship be- al., 2010]. To overcome these limitations, some authors have tween the corrected wind speed and power output. combined statistical and physical models, by using NWP To validate the proposed approach, two real world (Numerical Weather Prediction) data from a physical model datasets were used for model construction and test- as inputs to a statistical model [Salcedo-Sanz et al., 2009; ing. The simulation results were compared with the Al-Yahyai et al., 2010]. persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% A forecast model combined with NWP data was proposed [ ] improvement in forecasting accuracy (Mean Ab- by Vaccaro Vaccaro et al., 2011 , in which one-day-ahead solute Error) compared to the ANN model on one wind power forecasting is realized based on information dataset, and nearly 5% improvement on another. amalgamated from a local atmospheric model and measured data. Giorgi [Giorgi et al., 2011] developed a series of fore- cast systems by combining an Elman network and an MLP 1 Introduction network, and predicted power production of a wind farm with As wind is an effective renewable energy resource, the uti- three wind turbines at 5 time horizons: 1, 3, 6, 12 and 24 lization of wind energy has been growing rapidly around hours. The normalized absolute average error of the systems the world. With approximately 68 GW capacity of wind varied from 5.67% to 15.50% depending on forecast time and farms, China has the largest wind energy generation in the different combining ways of networks. world, and has continued with a high growth rate of 9% Recently, Gaussian Processes (GP) have been applied in the first half of 2012 [GWEC, 2012]. However, the in- broadly in many domains, including wind energy prediction. trinsically variable and uncontrollable characteristics of wind Jiang and Dong focused on very short term (< 30min) wind- pose several operational challenges. Thus wind power pre- speed prediction using GPs [Jiang et al., 2010]. They evalu- diction is an essential process for the maintenance of wind ated their model on real-world datasets, and found that the GP power units and energy reserve scheduling [Costa et al., 2008; performs better than ARMA (a simpler variant of ARIMA) Lei et al., 2009]. and Mycielski algorithms [Hocaoglu et al., 2009]. Short-term wind power forecasts with a prediction horizon In this paper, wind-farm datasets including Numerical from one hour to several days are critical to optimize wind Weather Prediction (NWP) results and measured data from farm maintenance and plan electricity reserves which im- a SCADA (Supervisory Control And Data Acquisition) sys- pact grid reliability and market-based ancillary service costs. tem are analyzed and used to develop wind power forecasting Broadly speaking, there are two approaches to short-term models over a horizon of up to one day, with a Gaussian Pro- wind power forecasting: statistical models and physical mod- cess (GP) method. The main innovations in this paper are els. The former uses only historical wind speed and power listed below: 2790 1. The predicted wind speed from an NWP model is cor- casting with the horizon from 1 to 24 hours for wind power, rected using a GP. This process helps to improve per- based on hourly sampled NWP data. formance compared with earlier methods for combining statistical and physical models. 3 Gaussian Process Methods 2. Automatic Relevance Determination was used for fea- ture selection in order to improve generalization perfor- Gaussian processes have been successfully applied to many mance. machine learning tasks. A systematic and detailed expla- nation of Gaussian process regression and Automatic Rev- 3. A censored Gaussian Process (CGP) method is applied elance Determination (ARD) can be found in Rasmussen’s to build the relationship between corrected wind speed book [C. Rasmussen, 2006]. The extension to censored data and wind power. The method accounts for the proba- was developed in [Groot, 2012]: here we will only provide a bilistic character of the values that are not known pre- brief description. cisely because of censoring. 4. A subset of high-wind speed data is treated separately, 3.1 Gaussian Processes because of its different characteristics based on analysis Consider a Gaussian process f(x) for a classic regression of the initial models. problem. Assuming we have a training set D of n obser- 5. Historical wind speed data from the SCADA system is vations, D = {(xi,yi)|i =1,...,n}, where x denotes an used as an additional input to the forecasting model for input vector and y denotes a scalar output, the task is to build 1–4 hours-ahead prediction, since we have proved this a function that satisfies to be effective in this range of time horizons. yi = f(xi)+i. (1) Section 2 describes the NWP data used to provide daily forecasts of meteorological variables. Section 3 defines the The additive noise is assumed to have a Gaussian distri- 2 standard and censored Gaussian Process and Automatic Rel- bution: ∼N(0,σn). Note that y is a linear combi- evance Determination (ARD) methods applied to build all the nation of Gaussian variables and hence is itself Gaussian. 2 regression models. Section 4 describes the whole detailed Therefore, we have p(y|X, k)=N (0,K + σnI), where modeling process from NWP data to forecast wind power re- Kij = k(xi,xj), and the joint distribution for a new input sults. Simulation results are presented and analyzed in Sec- x∗ can be written as tion 5. Finally, Section 6 includes the final conclusions. y K(X, X)+σ2 Ik(X, x ) ∼ 0, n ∗ , (2) f∗ k(x ,X) k(x ,x ) 2 NWP model and power forecasting ∗ ∗ ∗ T Numerical weather prediction uses hydrodynamic and ther- where k(X, x∗)=k(x∗,X) =[k(x1,x∗),...,k(xn,x∗)], modynamic models of the atmosphere to predict weather which we will write more briefly as k∗. Then according to based on certain initial-value and boundary conditions. In our the properties of joint Gaussian distributions, the prediction research, we use the WRF (Weather Research and Forecast- distribution of the target is given by a Gaussian distribution ing) NWP model. WRF was created through a partnership f¯ = kT (K + σ2 I)−1y that includes NCAR (National Center for Atmospheric Re- ∗ ∗ n T 2 −1 (3) search), NOAA (National Oceanic and Atmospheric Admin- V [f∗]=k(x∗,x∗) − k∗ (K + σnI) k∗. istration), and more than 150 other organizations and univer- sities in the United States and internationally, and is released As a result of the wind turbine control strategy, there is as a free model for public use [Hosansky, 2006]. always a defined upper limit C and lower limit of 0 for In general, short-term wind power forecasting needs pre- the power generation of each turbine. Therefore, in statis- dictions from a NWP model with high spatial resolution. The tical terminology, the true values (unrestricted power out- data from WRF isn’t suitable directly for this application, and put) are ‘censored’ in that they are not observed but are re- hence need to be interpolated both horizontally and vertically. placed by the threshold value. Assuming that the latent val- The NWP data used in this paper is produced and interpolated ues y∗ = f(x) can be realized by a Gaussian process, then to from the location in the WRF model, in our case from 5km- predict the actual output y, the influence of censoring should resolution, to the accurate geographical point and hub height be considered inside the model. To implement this considera- of each wind turbine. This prediction data is produced once tion, a censored GP model was developed in [Groot, 2012],in each day, and is usually available at 00:00 GMT. The data in- which the posterior distribution of y was formed by integrat- cluding wind speed and direction, temperature, humidity and ing out the censored distribution of latent variables and ap- pressure, is provided at an interval of 10 minutes for the fol- proximated using Expectation Propagation (EP).
Load more

Recommended publications
  • Weather and Climate: Changing Human Exposures K
    CHAPTER 2 Weather and climate: changing human exposures K. L. Ebi,1 L. O. Mearns,2 B. Nyenzi3 Introduction Research on the potential health effects of weather, climate variability and climate change requires understanding of the exposure of interest. Although often the terms weather and climate are used interchangeably, they actually represent different parts of the same spectrum. Weather is the complex and continuously changing condition of the atmosphere usually considered on a time-scale from minutes to weeks. The atmospheric variables that characterize weather include temperature, precipitation, humidity, pressure, and wind speed and direction. Climate is the average state of the atmosphere, and the associated characteristics of the underlying land or water, in a particular region over a par- ticular time-scale, usually considered over multiple years. Climate variability is the variation around the average climate, including seasonal variations as well as large-scale variations in atmospheric and ocean circulation such as the El Niño/Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO). Climate change operates over decades or longer time-scales. Research on the health impacts of climate variability and change aims to increase understanding of the potential risks and to identify effective adaptation options. Understanding the potential health consequences of climate change requires the development of empirical knowledge in three areas (1): 1. historical analogue studies to estimate, for specified populations, the risks of climate-sensitive diseases (including understanding the mechanism of effect) and to forecast the potential health effects of comparable exposures either in different geographical regions or in the future; 2. studies seeking early evidence of changes, in either health risk indicators or health status, occurring in response to actual climate change; 3.
    [Show full text]
  • Environmental Systems the Atmosphere and Hydrosphere
    Environmental Systems The atmosphere and hydrosphere THE ATMOSPHERE The atmosphere, the gaseous layer that surrounds the earth, formed over four billion years ago. During the evolution of the solid earth, volcanic eruptions released gases into the developing atmosphere. Assuming the outgassing was similar to that of modern volcanoes, the gases released included: water vapor (H2O), carbon monoxide (CO), carbon dioxide (CO2), hydrochloric acid (HCl), methane (CH4), ammonia (NH3), nitrogen (N2) and sulfur gases. The atmosphere was reducing because there was no free oxygen. Most of the hydrogen and helium that outgassed would have eventually escaped into outer space due to the inability of the earth's gravity to hold on to their small masses. There may have also been significant contributions of volatiles from the massive meteoritic bombardments known to have occurred early in the earth's history. Water vapor in the atmosphere condensed and rained down, of radiant energy in the atmosphere. The sun's radiation spans the eventually forming lakes and oceans. The oceans provided homes infrared, visible and ultraviolet light regions, while the earth's for the earliest organisms which were probably similar to radiation is mostly infrared. cyanobacteria. Oxygen was released into the atmosphere by these early organisms, and carbon became sequestered in sedimentary The vertical temperature profile of the atmosphere is variable and rocks. This led to our current oxidizing atmosphere, which is mostly depends upon the types of radiation that affect each atmospheric comprised of nitrogen (roughly 71 percent) and oxygen (roughly 28 layer. This, in turn, depends upon the chemical composition of that percent).
    [Show full text]
  • Wind Energy Forecasting: a Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy
    Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy Keith Parks Xcel Energy Denver, Colorado Yih-Huei Wan National Renewable Energy Laboratory Golden, Colorado Gerry Wiener and Yubao Liu University Corporation for Atmospheric Research (UCAR) Boulder, Colorado NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. S ubcontract Report NREL/SR-5500-52233 October 2011 Contract No. DE-AC36-08GO28308 Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy Keith Parks Xcel Energy Denver, Colorado Yih-Huei Wan National Renewable Energy Laboratory Golden, Colorado Gerry Wiener and Yubao Liu University Corporation for Atmospheric Research (UCAR) Boulder, Colorado NREL Technical Monitor: Erik Ela Prepared under Subcontract No. AFW-0-99427-01 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory Subcontract Report 1617 Cole Boulevard NREL/SR-5500-52233 Golden, Colorado 80401 October 2011 303-275-3000 • www.nrel.gov Contract No. DE-AC36-08GO28308 This publication received minimal editorial review at NREL. NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
    [Show full text]
  • Wind Characteristics 1 Meteorology of Wind
    Chapter 2—Wind Characteristics 2–1 WIND CHARACTERISTICS The wind blows to the south and goes round to the north:, round and round goes the wind, and on its circuits the wind returns. Ecclesiastes 1:6 The earth’s atmosphere can be modeled as a gigantic heat engine. It extracts energy from one reservoir (the sun) and delivers heat to another reservoir at a lower temperature (space). In the process, work is done on the gases in the atmosphere and upon the earth-atmosphere boundary. There will be regions where the air pressure is temporarily higher or lower than average. This difference in air pressure causes atmospheric gases or wind to flow from the region of higher pressure to that of lower pressure. These regions are typically hundreds of kilometers in diameter. Solar radiation, evaporation of water, cloud cover, and surface roughness all play important roles in determining the conditions of the atmosphere. The study of the interactions between these effects is a complex subject called meteorology, which is covered by many excellent textbooks.[4, 8, 20] Therefore only a brief introduction to that part of meteorology concerning the flow of wind will be given in this text. 1 METEOROLOGY OF WIND The basic driving force of air movement is a difference in air pressure between two regions. This air pressure is described by several physical laws. One of these is Boyle’s law, which states that the product of pressure and volume of a gas at a constant temperature must be a constant, or p1V1 = p2V2 (1) Another law is Charles’ law, which states that, for constant pressure, the volume of a gas varies directly with absolute temperature.
    [Show full text]
  • Characterisation of Intra-Hourly Wind Power Ramps at the Wind Farm Scale and Associated Processes
    Wind Energ. Sci., 6, 131–147, 2021 https://doi.org/10.5194/wes-6-131-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes Mathieu Pichault1, Claire Vincent2, Grant Skidmore1, and Jason Monty1 1Department of Mechanical Engineering, The University of Melbourne, Melbourne, Victoria 3010, Australia 2School of Earth Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia Correspondence: Mathieu Pichault ([email protected]) Received: 12 May 2020 – Discussion started: 5 June 2020 Revised: 15 September 2020 – Accepted: 8 December 2020 – Published: 19 January 2021 Abstract. One of the main factors contributing to wind power forecast inaccuracies is the occurrence of large changes in wind power output over a short amount of time, also called “ramp events”. In this paper, we assess the behaviour and causality of 1183 ramp events at a large wind farm site located in Victoria (southeast Australia). We address the relative importance of primary engineering and meteorological processes inducing ramps through an automatic ramp categorisation scheme. Ramp features such as ramp amplitude, shape, diurnal cycle and seasonality are further discussed, and several case studies are presented. It is shown that ramps at the study site are mostly associated with frontal activity (46 %) and that wind power fluctuations tend to plateau before and after the ramps. The research further demonstrates the wide range of temporal scales and behaviours inherent to intra-hourly wind power ramps at the wind farm scale. 1 Introduction hourly) ramp forecasts (Zhang et al., 2017; Cui et al., 2015; Gallego et al., 2015a).
    [Show full text]
  • 5 Minute Wind Forecasting Challenge: Exelon and GE's Predix
    The 5 Minute Wind Forecasting Challenge: Exelon and GE’s Predix At a Glance A move toward digital industrial transformation As a leading utility company with more than $31 billion in global Renewable Energy revenues in 2016 and over 32 gigawatts (GW) of total generation, Exelon knows the importance of taking a strategic view of digital transformation across its lines of business. Challenge Exelon sought to optimize wind power forecasting by predicting wind Exelon was developing strategies for managing its various generation ramp events, enabling the company to dispatch power that could not be assets across nuclear, fossil fuels, wind, hydro, and solar power as well monetized otherwise. The result is higher revenue for Exelon’s large-scale wind farm operations. as determining how it would leverage the enormous amount of data those assets would generate going forward. Solution GE and Exelon teams co-innovated to build a solution on Predix that In evaluating its strategies, the company reviewed its current increases wind forecasting accuracy by designing a new physical and statistical wind power forecast model that uses turbine data on-premises OT/IT infrastructure across its entire energy portfolio. together with weather forecasting data. This model now represents Business leaders looked at the system administration challenges the industry-leading forecasting solution (as measured by a substantial and costs they would face to maintain the current infrastructure, let reduction in under-forecasting). alone use it as a basis for driving new revenue across its business Results units. This assessment made digital transformation an even greater Exelon’s wind forecasting prediction accuracy grew signifcantly, enabling imperative, and inspired discussions about how Exelon could leverage higher energy capture valued at $2 million per year.
    [Show full text]
  • Weather & Climate
    Weather & Climate July 2018 “Weather is what you get; Climate is what you expect.” Weather consists of the short-term (minutes to days) variations in the atmosphere. Weather is expressed in terms of temperature, humidity, precipitation, cloudiness, visibility and wind. Climate is the slowly varying aspect of the atmosphere-hydrosphere-land surface system. It is typically characterized in terms of averages of specific states of the atmosphere, ocean, and land, including variables such as temperature (land, ocean, and atmosphere), salinity (oceans), soil moisture (land), wind speed and direction (atmosphere), and current strength and direction (oceans). Example of Weather vs. Climate The actual observed temperatures on any given day are considered weather, whereas long-term averages based on observed temperatures are considered climate. For example, climate averages provide estimates of the maximum and minimum temperatures typical of a given location primarily based on analysis of historical data. Consider the evolution of daily average temperature near Washington DC (40N, 77.5W). The black line is the climatological average for the period 1979-1995. The actual daily temperatures (weather) for 1 January to 31 December 2009 are superposed, with red indicating warmer-than-average and blue indicating cooler-than-average conditions. Departures from the average are generally largest during winter and smallest during summer at this location. Weather Forecasts and Climate Predictions / Projections Weather forecasts are assessments of the future state of the atmosphere with respect to conditions such as precipitation, clouds, temperature, humidity and winds. Climate predictions are usually expressed in probabilistic terms (e.g. probability of warmer or wetter than average conditions) for periods such as weeks, months or seasons.
    [Show full text]
  • NAWEA 2015 Symposium Book of Abstracts
    NAWEA 2015 Symposium Tuesday 09 June 2015 - Thursday 11 June 2015 Virginia Tech Campus Goodwin Hall Book of Abstracts i Table of contents Wind Farm Layout Optimization Considering Turbine Selection and Hub Height Variation ....................... 1 Graduate Education Programs in Wind Energy ................................................................................. 2 Benefits of vertically-staggered wind turbines from theoretical analysis and Large-Eddy Simulations ........... 3 On the Effects of Directional Bin Size when Simulating Large Offshore Wind Farms with CFD ................... 7 A game-theoretic framework to investigate the conditions for cooperation between energy storage operators and wind power producers ............................................................................................................ 9 Detection of Wake Impingement in Support of Wind Plant Control ....................................................... 11 Sensitivity of Wind Turbine Airfoil Sections to Geometry Variations Inherent in Modular Blades ................ 15 Exploiting the Characteristics of Kevlar-Wall Wind Tunnels for Conventional Aerodynamic Measurements with Implications for Testing of Wind Turbine Sections ...................................................................... 16 Spatially Resolved Wind Tunnel Wake Measurements at High Angles of Attack and High Reynolds Numbers Using a Laser-Based Velocimeter .................................................................................................... 17 Windtelligence:
    [Show full text]
  • ESSENTIALS of METEOROLOGY (7Th Ed.) GLOSSARY
    ESSENTIALS OF METEOROLOGY (7th ed.) GLOSSARY Chapter 1 Aerosols Tiny suspended solid particles (dust, smoke, etc.) or liquid droplets that enter the atmosphere from either natural or human (anthropogenic) sources, such as the burning of fossil fuels. Sulfur-containing fossil fuels, such as coal, produce sulfate aerosols. Air density The ratio of the mass of a substance to the volume occupied by it. Air density is usually expressed as g/cm3 or kg/m3. Also See Density. Air pressure The pressure exerted by the mass of air above a given point, usually expressed in millibars (mb), inches of (atmospheric mercury (Hg) or in hectopascals (hPa). pressure) Atmosphere The envelope of gases that surround a planet and are held to it by the planet's gravitational attraction. The earth's atmosphere is mainly nitrogen and oxygen. Carbon dioxide (CO2) A colorless, odorless gas whose concentration is about 0.039 percent (390 ppm) in a volume of air near sea level. It is a selective absorber of infrared radiation and, consequently, it is important in the earth's atmospheric greenhouse effect. Solid CO2 is called dry ice. Climate The accumulation of daily and seasonal weather events over a long period of time. Front The transition zone between two distinct air masses. Hurricane A tropical cyclone having winds in excess of 64 knots (74 mi/hr). Ionosphere An electrified region of the upper atmosphere where fairly large concentrations of ions and free electrons exist. Lapse rate The rate at which an atmospheric variable (usually temperature) decreases with height. (See Environmental lapse rate.) Mesosphere The atmospheric layer between the stratosphere and the thermosphere.
    [Show full text]
  • Land-Based Wind Market Report: 2021 Edition This Report Is Being Disseminated by the U.S
    Land-Based Wind Market Report: 2021 Edition This report is being disseminated by the U.S. Department of Energy (DOE). As such, this document was prepared in compliance with Section 515 of the Treasury and General Government Appropriations Act for fiscal year 2001 (public law 106-554) and information quality guidelines issued by DOE. Though this report does not constitute “influential” information, as that term is defined in DOE’s information quality guidelines or the Office of Management and Budget’s Information Quality Bulletin for Peer Review, the study was reviewed both internally and externally prior to publication. For purposes of external review, the study benefited from the advice and comments of 11 industry stakeholders, U.S. Government employees, and national laboratory staff. NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Available electronically at SciTech Connect: http://www.osti.gov/scitech Available for a processing fee to U.S.
    [Show full text]
  • A Critical Review of Wind Power Forecasting Methods—Past, Present and Future
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Enlighten energies Review A Critical Review of Wind Power Forecasting Methods—Past, Present and Future Shahram Hanifi 1, Xiaolei Liu 1,* , Zi Lin 2,3,* and Saeid Lotfian 2 1 James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; s.hanifi[email protected] 2 Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK; saeid.lotfi[email protected] 3 Department of Mechanical & Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK * Correspondence: [email protected] (X.L.); [email protected] (Z.L.) Received: 16 June 2020; Accepted: 20 July 2020; Published: 22 July 2020 Abstract: The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc.
    [Show full text]
  • Energia Eólica Panfleto Dez07
    EEEEnnnneeeerrrrggggiiiiaaaa EEEEóóóólllliiiiccccaaaa DDDeeezzzeeemmmbbbrrrooo 222000000777 INEGI O INEGI - Instituto de Engenhar ia Mecânica e Gestão Industrial tem procurado , desde a sua criação, fomentar e empenhar -se no estudo da utilização das fontes de energia não convencionais, e na poupança e utilização racional da energia. Dedicando -se ao estudo do aproveitamento da Energia Eólica, o INEGI pretende apoiar o desenvolvimento das energias renováveis, contribuindo para a diversificação dos recursos primários usados na geração de elect ricidade e para a preservação do meio ambiente. Desde 1991, o INEGI tem uma equipa especialmente dedicada à Energia Eólica . Para além do planeamento e condução de campanhas de avaliação do recurso eólico, o INEGI disponibiliza hoje diversos outros serviço s relacionados com o tema, como sejam os cálculos de produtividade e a optimização da configuração de parques eólicos, a realização de estudos de viabilidade técnico -económica de projectos, o apoio na elabor ação de cadernos de encargos, apreciação de propo stas e comparação de soluções, a avaliação do desempenho de aerogeradores, a verificação de garantias de produção , a realização de auditorias e avaliaç ões de projectos para instituições financeiras e outras e o apoio em acções de planeamento e ordenamento. Inicialmente restritas ao Norte e Centro de Portugal, as actividades do INEGI neste domínio estendem -se actualmente a todo o país e, desde 1999, também ao estrangeiro. Fazendo uso da experiência adquirida pela participação dos colaboradores em projectos i nternacionais, foram adoptadas metodologias e técnicas de operação que permitem fornecer aos seus clientes serviços de qualidade. Através do contacto com institutos congéneres e consultores de toda a Europa, e da participação em conferências e seminários i nternacionais, procura o INEGI manter -se actualizado nos recursos e nas práticas seguidas.
    [Show full text]