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

Tendencias recientes en el pronóstico de velocidad de viento para generación eólica

Este documento tiene como objetivo presentar un marco unificado para discutir, resumir y organizar los principales avances en pronóstico de velocidad de viento para generación eólica utilizando un método auditable, ordenado y reproducible. Los principales hallazgos fueron: La mayor parte de los trabajos provienen de China y Estados Unidos, las series de tiempo usadas poseen una longitud de menos de un año, comúnmente el pronóstico es realizado en un rango de 1 hora a 48 horas hacia adelante. Muchos estudios usan solamente modelos autoregresivos (Lineares y no lineares) o en muchos casos una sola variable explicatoria. Usualmente la variable pronosticada es la velocidad de viento u la potencia generada. La revisión muestra una tendencia en la que los autores están experimentando con modelos híbridos para obtener las ventajas de cada método utilizado, también, una tendencia a utilizar métodos clásicos como redes neuronales, máquinas de vectores de soporte y modelos autorregresivosAbstract: This document aims to provide a unified frame for discussing, summarizing and organizing the main advances in wind power forecasting using an auditable, orderly and reproducible method. Our main findings are the following: most of works forecasting time series from China and United States; time series data usually cover information with a length lower than a year of data. Commonly, the forecast is done for 1 to 48 hours ahead. Many studies using only autorregresive models (linear or no linear) or, in many cases, one explanatory variable. Usually, the variables forecasted are speed and power. The review shows a tendency in which the authors are experimenting with hybrid models to obtain the advantages of each method used, also, a trend to use classical methods such as neural networks, Support Vector Machines and autoregressive models.Maestrí

A review of very short-term wind and solar power forecasting

Installed capacities of wind and solar power have grown rapidly over recent years, and the pool of literature on very short-term (minutes- to hours-ahead) wind and solar forecasting has grown in line with this. This paper reviews established and emerging approaches to provide an up-to-date view of the field. Knowledge transfer between wind and solar forecasting has benefited the field and is discussed, and new opportunities are identified, particularly regarding use of remote sensing technology. Forecasting methodologies and study design are compared and recommendations for high quality, reproducible results are presented. In particular, the choice of suitable benchmarks and use of sufficiently long datasets is highlighted. A case study of three distinct approaches to probabilistic wind power forecasting is presented using an open dataset. The case study provides an example of exemplary forecast evaluation, and open source code allows for its reproduction and use in future work

Topics in high dimensional energy forecasting

The forecasting of future energy consumption and generation is now an essential part of power system operation. In networks with high renewable power penetration, forecasts are used to help maintain security of supply and to operate the system efficiently. Historically, uncertainties have always been present in the demand side of the network, they are now also present in the generation side with the growth of weather dependent renewables. Here, we focus on forecasting for wind energy applications at the day(s)- ahead scale. Most of the work developed is for power forecasting, although we also identify an emerging opportunity in access forecasting for offshore operations. Power forecasts are used by traders, power system operators, and asset owners to optimise decision making based on future generation. Several novel methodologies are presented based on post–processing Numerical Weather Predictions (NWP) with measured data, using modern statistical learning techniques; they are linked with the increasingly relevant challenge of dealing with high-dimensional data. The term ‘high-dimensional’ means different things to different people, depending on their background. To statisticians high dimensionaility occurs when the dimensions of the problem are greater than the number of observations, i.e. the classic p >> n problem, an example of which can be found in Chapter 7. In this work we take the more general view that a high dimensional dataset is one with a high number of attributes or features. In wind energy forecasting applications, this can occur in the input and/or output variable space. For example, multivariate forecasting of spatially distributed wind farms can be a potentially very-high dimensional problem, but so is feature engineering using ultra-high resolution NWP in this framework. Most of the work in this thesis is based on various forms of probabilistic forecasting Probabilistic forecasts are essential for risk-management, but also to risk-neutral participants in asymmetrically penalised electricity markets. Uncertainty is always present, it is merely hidden in deterministic, i.e. point, forecasts. This aspect of forecasting has been the subject of a concerted research effort over the last few years in the energy forecasting literature. However, we identify and address gaps in the literature related to dealing with high dimensional data in both the input and output side of the modelling chain. It is not necessarily given that increasing the resolution of the weather forecast increases the skill, and therefore reduces errors associated with the forecast. In fact and when regarding typical average scoring rules, they often perform worse than smoother forecasts from lower-resolution models due to spatial and/or temporal displacement errors. Here, we evaluate the potential of using ultra high resolution weather models for offshore power forecasting, using feature engineering and modern statistical learning techniques. Two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data are proposed. Although standard resolution NWP data is used, high dimensionality is now present in the output variable space; the two methods scale by the number of turbines present in the wind farm, although to a different extent. A methodology for regime-switching multivariate wind power forecasting is also elaborated, with a case study demonstrated on 92 wind balancing mechanism units connected to the GB network. Finally, we look at an emerging topic in energy forecasting: offshore access forecasting. Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. We describe a novel methodology for producing probabilistic forecasts of access conditions during crew transfers.The forecasting of future energy consumption and generation is now an essential part of power system operation. In networks with high renewable power penetration, forecasts are used to help maintain security of supply and to operate the system efficiently. Historically, uncertainties have always been present in the demand side of the network, they are now also present in the generation side with the growth of weather dependent renewables. Here, we focus on forecasting for wind energy applications at the day(s)- ahead scale. Most of the work developed is for power forecasting, although we also identify an emerging opportunity in access forecasting for offshore operations. Power forecasts are used by traders, power system operators, and asset owners to optimise decision making based on future generation. Several novel methodologies are presented based on post–processing Numerical Weather Predictions (NWP) with measured data, using modern statistical learning techniques; they are linked with the increasingly relevant challenge of dealing with high-dimensional data. The term ‘high-dimensional’ means different things to different people, depending on their background. To statisticians high dimensionaility occurs when the dimensions of the problem are greater than the number of observations, i.e. the classic p >> n problem, an example of which can be found in Chapter 7. In this work we take the more general view that a high dimensional dataset is one with a high number of attributes or features. In wind energy forecasting applications, this can occur in the input and/or output variable space. For example, multivariate forecasting of spatially distributed wind farms can be a potentially very-high dimensional problem, but so is feature engineering using ultra-high resolution NWP in this framework. Most of the work in this thesis is based on various forms of probabilistic forecasting Probabilistic forecasts are essential for risk-management, but also to risk-neutral participants in asymmetrically penalised electricity markets. Uncertainty is always present, it is merely hidden in deterministic, i.e. point, forecasts. This aspect of forecasting has been the subject of a concerted research effort over the last few years in the energy forecasting literature. However, we identify and address gaps in the literature related to dealing with high dimensional data in both the input and output side of the modelling chain. It is not necessarily given that increasing the resolution of the weather forecast increases the skill, and therefore reduces errors associated with the forecast. In fact and when regarding typical average scoring rules, they often perform worse than smoother forecasts from lower-resolution models due to spatial and/or temporal displacement errors. Here, we evaluate the potential of using ultra high resolution weather models for offshore power forecasting, using feature engineering and modern statistical learning techniques. Two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data are proposed. Although standard resolution NWP data is used, high dimensionality is now present in the output variable space; the two methods scale by the number of turbines present in the wind farm, although to a different extent. A methodology for regime-switching multivariate wind power forecasting is also elaborated, with a case study demonstrated on 92 wind balancing mechanism units connected to the GB network. Finally, we look at an emerging topic in energy forecasting: offshore access forecasting. Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. We describe a novel methodology for producing probabilistic forecasts of access conditions during crew transfers

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Amélioration des prévisions immédiates du vent et de la production éolienne

Le vent est une variable météorologique extrêmement difficile à prédire. Augmenter la précision des prévisions de vent à court terme (jusqu’à 12 heures à l’avance) est nécessaire pour optimiser les opérations des parcs éoliens, maximiser leur rendement, et favoriser leur développement. L’objectif de ce projet est donc d’améliorer les techniques existantes de prévision des vitesses de vent et de la puissance produite par les éoliennes. Suite à une revue de littérature approfondie des techniques de prévision du vent, le filtre de Kalman est identifié comme l’approche la plus prometteuse. Il s’agit d’une méthode adaptative de correction de l’erreur associée aux modèles de prévision physiques, qui peut être appliquée presque instantanément puisqu’elle ne nécessite pas de longue période d’entraînement sur un ensemble de données historiques. La plupart des approches récentes reposent sur les prévisions de vitesse de vent uniquement comme paramètre d’entrée, alors qu’il a été mentionné à plusieurs reprises dans la littérature que l’ajout d’autres variables, notamment la direction du vent, devrait être considéré pour améliorer la précision des modèles. Il a déjà été démontré pour certaines approches (par exemple, les réseaux de neurones artificiels), que la direction permettait de réduire l’erreur moyenne. En revanche, l’ajout de la direction dans les modèles de filtre de Kalman appliqués à la vitesse du vent ou à la puissance n’a pas été étudié à ce jour. Dans le cadre de ce projet, une nouvelle méthode est proposée, selon laquelle des filtres de Kalman sont modélisés de façon à tenir compte de la direction du vent comme paramètre d’entrée. Le biais entre les prévisions brutes d’un modèle de prévision numérique du temps et les observations est modélisé de façon non linéaire, en fonction de la vitesse et la direction du vent. Pour la prévision de la puissance, deux approches sont développées pour les cas où des observations de vitesse de vent ne sont pas disponibles. D’une part, un filtre de Kalman est appliqué à la puissance directement afin d’estimer son biais. D’autre part, une technique selon laquelle les puissances observées sont converties en vitesses de vent fictives par la courbe de puissance inverse est introduite, dans le but d’appliquer les filtres sur la vitesse du vent. Les données historiques de prévision et d’observation de 20 sites situés en Europe et en Amérique du Nord sont utilisées pour analyser la performance des modèles. Afin de quantifier la précision des différents modèles, plusieurs indicateurs sont calculés, notamment le biais, le Mean Absolute Error (MAE) et le Root-Mean-Square Error (RMSE).----------Abstract Short term wind speed and wind power production have been studied thoroughly in the last decades. Wind is a highly fluctuating meteorological parameter, and improving the accuracy of wind speed forecasts is essential to favor the expansion of the wind energy sector, and thus lower our dependence on fossil fuels. Essentially, enhancing the quality of short term wind speed forecasts (up to 12 hours ahead) is necessary in order to optimize wind farm operations and maximize their economic profitability. The purpose of this project is therefore to improve existing wind speed and wind power output prediction techniques. Following a comprehensive literature review, the Kalman Filter appears as the most promising approach. It is an adaptive method that corrects the bias associated to the Numerical Weather Prediction (NWP) models, and which can be applied almost instantly without prior training on an extensive set of historical data. Most of the recent techniques rely exclusively on wind speed data input, while it is mentioned numerous times in literature that more input parameters, particularly wind direction, should be considered in order to improve forecast accuracy. It has already been shown that for some approaches, such as Artificial Neural Networks, the addition of wind direction leads to reduced forecast errors. However, introducing wind direction into the Kalman Filter models has not been studied to this date. In this project, a novel approach is introduced, where Kalman Filter algorithms are used to estimate the bias of NWP forecasts as non-linear functions of wind speed and wind direction. As for wind power forecast, two techniques are introduced for wind farms where power measurement is the sole available data. Firstly, wind power bias is estimated by a Kalman Filter applied to power forecasts. Secondly, an Inverse Power Curve Transformation is used to convert observed power production into estimated wind speed values to then apply the filter directly on wind speeds. The forecast values of wind speed are then converted back into power forecasts with the usual power curve. Historical measurement and forecast data from 20 wind farms located throughout Europe and North America have been made available for this study. In order to quantify the accuracy of the models developed in this research, various performance indicators were used, such as bias, Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). For Kalman Filters applied on wind speed, the first order polynomial has shown the best results, whereas for power output, higher order polynomials have shown better performances in correcting the bias. Furthermore, the addition of wind direction into the bias modeling allows achieving higher accuracy for all of the studied approaches

Robust data cleaning procedure for large scale medium voltage distribution networks feeders

Relatively little attention has been given to the short-term load forecasting problem of primary substations mainly because load forecasts were not essential to secure the operation of passive distribution networks. With the increasing uptake of intermittent generations, distribution networks are becoming active since power flows can change direction in a somewhat volatile fashion. The volatility of power flows introduces operational constraints on voltage control, system fault levels, thermal constraints, systems losses and high reverse power flows. Today, greater observability of the networks is essential to maintain a safe overall system and to maximise the utilisation of existing assets. Hence, to identify and anticipate for any forthcoming critical operational conditions, networks operators are compelled to broaden their visibility of the networks to time horizons that include not only real-time information but also hour-ahead and day-ahead forecasts. With this change in paradigm, progressively, large scales of short-term load forecasters is integrated as an essential component of distribution networks' control and planning tools. The data acquisition of large scale real-world data is prone to errors; anomalies in data sets can lead to erroneous forecasting outcomes. Hence, data cleansing is an essential first step in data-driven learning techniques. Data cleansing is a labour-intensive and time-consuming task for the following reasons: 1) to select a suitable cleansing method is not trivial 2) to generalise or automate a cleansing procedure is challenging, 3) there is a risk to introduce new errors in the data. This thesis attempts to maximise the performance of large scale forecasting models by addressing the quality of the modelling data. Thus, the objectives of this research are to identify the bad data quality causes, design an automatic data cleansing procedure suitable for large scale distribution network datasets and, to propose a rigorous framework for modelling MV distribution network feeders time series with deep learning architecture. The thesis discusses in detail the challenges in handling and modelling real-world distribution feeders time series. It also discusses a robust technique to detect outliers in the presence of level-shifts, and suitable missing values imputation techniques. All the concepts have been demonstrated on large real-world distribution network data.Open Acces

Time Series Modelling

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples