Forecasting wind energy for a data center

Abstract. Data centers are increasingly using renewables such as wind and solar energy. RISE’s ICE data center has already solar panels and is now studying impact of adding a wind turbine into their microgrid. In this thesis, a machine learning model was developed to forecast wind power production for the data center. Data center in Luleå has several applications to utilize wind power forecasting. Renewable energy sources are intermittent, so accurate forecasting of output power reduces a need for additional balancing of energy and reserve power in an electricity grid. Renewable energy can be reserved from market for next hour or next day to maximize its use. Forecasting from 30 min to 6 hours ahead allows job scheduling to optimize usage of renewables and to reduce power consumption. Data center may target to minimize electricity cost or maximize usage of renewables for lower greenhouse gas emissions. Smart microgrid based on artificial intelligence is the way to implement the applications. Two open data sets from India and Sweden have been used in the research. The data available supports choosing of a statistical model. Random forest regression was the model used in the research. Data from India enabled to develop a model for one wind turbine. Developed model forecasted output power well. Swedish data set is from EEM20 competition, it included total wind power production in Sweden and had to be applied to approximate production of one wind turbine in Luleå. To achieve the goal output power of Luleå price region was averaged, and location for the simulation was chosen to be near Luleå. As expected, the accuracy of forecasting with Swedish data was reasonable, but approximations done reduced it. The developed model was applied to RISE’s ICE data center. Validation has been done, but final testing will take place in RISE’s simulation environment. In general, data from northern Sweden is not openly available for wind power forecasting. In addition, any scientific articles covering the geographical area were not found while working on literature review. The study with Swedish competition data gave understanding, which variables are significant in northern Sweden and about their relative importances. Wind gust is such a variable. Using two data sets from different geographical locations proved that climate has a major impact on performance of the trained model. Thus, it is reasonable to use the trained model in locations with similar weather conditions only.Tuulienergian ennustaminen datakeskusta varten. Tiivistelmä. Datakeskukset käyttävät uusiutuvia energialähteitä yhä enemmän. Tällaisia lähteitä ovat mm. tuuli- ja aurinkoenergia. RISE:n ICE datakeskuksella Luulajassa on jo aurinkopaneelit käytössä, ja nyt tutkitaan tuulimyllyn lisäämisen vaikutusta mikroverkkoon. Tässä työssä kehitettiin koneoppimismalli tuulivoiman tuotannon ennustamiseksi datakeskusta varten. Datakeskuksella on useita sovelluksia tuulienergian ennustamisen hyödyntämiseksi. Uusiutuvat energialähteet ovat luonteeltaan vaihtelevia, joten tuotetun tehon tarkka ennustaminen vähentää ylimääräisen säätämisen ja reservitehon tarvetta sähköverkossa yleensäkin. Datakeskus voi varata uusiutuvaa energiaa markkinoilta seuraavaksi tunniksi tai päiväksi uusiutuvan energian käytön maksimoimiseksi. Ennustaminen 30 minuutista 6 tuntiin etukäteen mahdollistaa työjonon aikatauluttamisen uusiutuvien käytön optimoimiseksi ja vähentää tehonkulutusta. Datakeskus voi pyrkiä minimoimaan sähkön käytön kustannuksia, tai pienentämään kasvihuonekaasujen päästöjä käyttämällä mahdollisimman paljon uusiutuvaa energiaa. Tekoälyyn perustuva älykäs mikroverkko on tapa toteuttaa edellä mainitut sovellukset. Tutkimuksessa on käytetty kahta avointa tietoainestoa Intiasta ja Ruotsista. Saatavilla oleva data tukee tilastollisen ennustemallin valintaa. Tässä työssä käytettiin satunnaismetsämenetelmää. Intian dataa käytettiin mallin kehityksessä yhtä tuulimyllyä varten. Kehitetty malli ennusti tuotetun tehon hyvin. Ruotsalainen data perustuu EEM20-kilpailuun, jossa arvioitiin koko Ruotsin tuulivoiman tuotantoa. Sitä olikin sovellettava Luulajassa olevan yhden tuulimyllyn tuotannon arvioimiseksi. Luulajan hinta-alueen tuottama teho keskiarvoistettiin, ja ennustamista varten valittiin maantieteellinen paikka läheltä Luulajaa. Kuten oli odotettavissa, soveltamisessa tehdyt likiarvoistukset pienensivät ennustamisen tarkkuutta, jota voidaan kuitenkin pitää kohtuullisena. Kehitettyä mallia sovellettiin RISE:n ICE datakeskusta varten. Algoritmin validointi on suoritettu, mutta lopullinen testaus tehdään RISE:n simulointiympäristössä. Yleisesti ennustamiseen soveltuvaa dataa ei ole Pohjois-Ruotsista tarjolla. Tieteellisiä artikkeleita ko. maantieteelliseltä alueelta ei löytynyt kirjallisuustutkimusta tehtäessä. Tutkimus ruotsalaisella datalla toi ymmärrystä siihen, mitkä muuttujat ovat merkittäviä Pohjois-Ruotsin alueella sekä niiden suhteellisesta merkityksestä. Kahden eri maantieteellisen alueen tietoaineiston käyttö osoitti, että ilmastolla on huomattava vaikutus koulutetun mallin suorituskykyyn. Näin onkin mielekästä käyttää koulutettua mallia vain sellaisilla alueilla, joiden sääolosuhteet ovat samankaltaiset

Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

Condition-based maintenance of wind turbine blades

The blades of offshore wind farms (OWTs) are susceptible to a wide variety of diverse sources of damage. Internal impacts are caused primarily by structure deterioration, so even though outer consequences are the consequence of harsh marine ecosystems. We examine condition-based maintenance (CBM) for a multiblade OWT system that is exposed to environmental shocks in this work. In recent years, there has been a significant rise in the number of wind turbines operating offshore that make use of CBMs. The gearbox, generator, and drive train all have their own vibration-based monitoring systems, which form most of their foundation. For the blades, drive train, tower, and foundation, a cost analysis of the various widely viable CBM systems as well as their individual prices has been done. The purpose of this article is to investigate the potential benefits that may result from using these supplementary systems in the maintenance strategy. Along with providing a theoretical foundation, this article reviews the previous research that has been conducted on CBM of OWT blades. Utilizing the data collected from condition monitoring, an artificial neural network is employed to provide predictions on the remaining life. For the purpose of assessing and forecasting the cost and efficacy of CBM, a simple tool that is based on artificial neural networks (ANN) has been developed. A CBM technique that is well-established and is based on data from condition monitoring is used to reduce cost of maintenance. This can be accomplished by reducing malfunctions, cutting down on service interruption, and reducing the number of unnecessary maintenance works. In MATLAB, an ANN is used to research both the failure replacement cost and the preventative maintenance cost. In addition to this, a technique for optimization is carried out to gain the optimal threshold values. There is a significant opportunity to save costs by improving how choices are made on maintenance to make the operations more cost-effective. In this research, a technique to optimizing CBM program for elements whose deterioration may be characterized according to the level of damage that it has sustained is presented. The strategy may be used for maintenance that is based on inspections as well as maintenance that is based on online condition monitoring systems

A data-mining approach for wind turbine fault detection based on scada data analysis using artificial neural networks

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20-30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm

Machine Learning based Wind Power Forecasting for Operational Decision Support

To utilize renewable energy efficiently to meet the needs of mankind's living demands becomes an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warning. However, large-scale development of hydropower increases greenhouse gas emissions and greenhouse effects. This research is related to knowledge of wind power forecasting (WPF) and machine learning (ML). This research is built around one central research question: How to improve the accuracy of WPF by using AI methods? A pilot conceptual system combining meteorological information and operations management has been formulated. The main contribution is visualized in a proposed new framework, named Meteorological Information Service Decision Support System, consisting of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system utilizes meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for WPEs based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset. Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm, in terms of RMSE, MAE and R2 compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time while comparing to the other algorithms in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of machine learning (ML), in improving local weather forecast on the coding platform of Python. The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. Findings from this research contribute to WPF in WPEs. The main contribution of this research is achieving decision optimization on a decision support system by using ML. It was concluded that the proposed system is very promising for potential applications in wind (power) energy management

Climate Services for Resilient Development (CSRD) Partnership’s work in Latin America

The Climate Services for Resilient Development (CSRD) Partnership is a private-public collaboration led by USAID, which aims to increase resilience to climate change in developing countries through the development and dissemination of climate services. The partnership began with initial projects in three countries: Colombia, Ethiopia, and Bangladesh. The International Center for Tropical Agriculture (CIAT) was the lead organization for the Colombian CSRD efforts – which then expanded to encompass work in the whole Latin American region

Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model

Wind power plays a leading role in the development of renewable energy. However, the random nature of wind turbine power and its associated uncertainty create challenges in dispatching this power effectively in the power system, which can result in unnecessary curtailment of the wind turbine power. Improving the accuracy of wind turbine power forecasting is an effective measure for resolving such problems. This study uses a deep learning network to forecast the wind turbine power based on a long short-term memory network (LSTM) algorithm and uses the Gaussian mixture model (GMM) to analyze the error distribution characteristics of short-term wind turbine power forecasting. The LSTM algorithm is used to forecast the power and uncertainties for three wind turbines within a wind farm. According to numerical weather prediction (NWP) data and historical power data for three turbines, the forecasting accuracy of the turbine with the largest number of training samples is the best of the three. For one of the turbines, the LSTM, radial basis function (RBF), wavelet, deep belief network (DBN), back propagation neural networks (BPNN), and Elman neural network (ELMAN) have been used to forecast the wind turbine power. This study compares the results and demonstrates that LSTM can greatly improve the forecasting accuracy. Moreover, this study obtains different confidence intervals for the three units according to the GMM, mixture density neural network (MDN), and relevance vector machine (RVM) model results. The LSTM method is shown to have higher accuracy and faster convergence than the other methods. However, the GMM method has better performance and evaluation than other methods and thus has practical application value for wind turbine power dispatching