Uncertainty in wind energy forecasting

Wind energy is the energy source that contributes most to the renewable energy mix of European countries. While there are good wind resources throughout Europe, the intermittency of the wind represents a major problem for the deployment of wind energy into the electricity networks. To ensure grid security a Transmission System Operator needs today for each kilowatt of wind energy either an equal amount of spinning reserve or a forecasting system that can predict the amount of energy that will be produced from wind over a period of 1 to 48 hours. In the range from 5m/s to 15m/s a wind turbine’s production increases with a power of three. For this reason, a Transmission System Operator requires an accuracy for wind speed forecasts of 1m/s in this wind speed range. Forecasting wind energy with a numerical weather prediction model in this context builds the background of this work. The author’s goal was to present a pragmatic solution to this specific problem in the ”real world”. This work therefore has to be seen in a technical context and hence does not provide nor intends to provide a general overview of the benefits and drawbacks of wind energy as a renewable energy source. In the first part of this work the accuracy requirements of the energy sector for wind speed predictions from numerical weather prediction models are described and analysed. A unique set of numerical experiments has been carried out in collaboration with the Danish Meteorological Institute to investigate the forecast quality of an operational numerical weather prediction model for this purpose. The results of this investigation revealed that the accuracy requirements for wind speed and wind power forecasts from today’s numerical weather prediction models can only be met at certain times. This means that the uncertainty of the forecast quality becomes a parameter that is as important as the wind speed and wind power itself. To quantify the uncertainty of a forecast valid for tomorrow requires an ensemble of forecasts. In the second part of this work such an ensemble of forecasts was designed and verified for its ability to quantify the forecast error. This was accomplished by correlating the measured error and the forecasted uncertainty on area integrated wind speed and wind power in Denmark and Ireland. A correlation of 93% was achieved in these areas. This method cannot solve the accuracy requirements of the energy sector. By knowing the uncertainty of the forecasts, the focus can however be put on the accuracy requirements at times when it is possible to accurately predict the weather. Thus, this result presents a major step forward in making wind energy a compatible energy source in the future.

Transport energy demand: techno-economic modelling and scenarios for Irish climate policy

The case for energy policy modelling is strong in Ireland, where stringent EU climate targets are projected to be overshot by 2015. Policy targets aiming to deliver greenhouse gas and renewable energy targets have been made, but it is unclear what savings are to be achieved and from which sectors. Concurrently, the growth of personal mobility has caused an astonishing increase in CO2 emissions from private cars in Ireland, a 37% rise between 2000 and 2008, and while there have been improvements in the efficiency of car technology, there was no decrease in the energy intensity of the car fleet in the same period. This thesis increases the capacity for evidenced-based policymaking in Ireland by developing techno-economic transport energy models and using them to analyse historical trends and to project possible future scenarios. A central focus of this thesis is to understand the effect of the car fleet‘s evolving technical characteristics on energy demand. A car stock model is developed to analyse this question from three angles: Firstly, analysis of car registration and activity data between 2000 and 2008 examines the trends which brought about the surge in energy demand. Secondly, the car stock is modelled into the future and is used to populate a baseline “no new policy” scenario, looking at the impact of recent (2008-2011) policy and purchasing developments on projected energy demand and emissions. Thirdly, a range of technology efficiency, fuel switching and behavioural scenarios are developed up to 2025 in order to indicate the emissions abatement and renewable energy penetration potential from alternative policy packages. In particular, an ambitious car fleet electrification target for Ireland is examined. The car stock model‘s functionality is extended by linking it with other models: LEAP-Ireland, a bottom-up energy demand model for all energy sectors in the country; Irish TIMES, a linear optimisation energy system model; and COPERT, a pollution model. The methodology is also adapted to analyse trends in freight energy demand in a similar way. Finally, this thesis addresses the gap in the representation of travel behaviour in linear energy systems models. A novel methodology is developed and case studies for Ireland and California are presented using the TIMES model. Transport Energy

The political and economic context of Keynes’s 1933 Finlay lecture: transforming a business practitioner’s ways of knowing

This thesis is structured in the format of a three part Portfolio of Exploration to facilitate transformation in my ways of knowing to enhance an experienced business practitioner’s capabilities and effectiveness. A key factor in my ways of knowing, as opposed to what I know, is my exploration of context and assumptions. By interacting with my cultural, intellectual, economic, and social history, I seek to become critically aware of the biographical, historical, and cultural context of my beliefs and feelings about myself. This Portfolio is not exclusively for historians of economics or historians of ideas but also for those interested in becoming more aware of how these culturally assimilated frames of reference and bundles of assumptions that influence the way they perceive, think, decide, feel and interpret their experiences in order to operate more effectively in their professional and organisational lives. In the first part of my Portfolio, I outline and reflect upon my Portfolio’s overarching theory of adult development; the writings of Harvard’s Robert Kegan and Columbia University’s Jack Mezirow. The second part delves further into how meaning-making, the activity of how one organises and makes sense of the world and how meaning-making evolves to different levels of complexity. I explore how past experience and our interpretations of history influences our understandings since all perception is inevitably tinged with bias and entrenched ‘theory-laden’ assumptions. In my third part, I explore the 1933 inaugural University College Dublin Finlay Lecture delivered by economist John Maynard Keynes. My findings provide a new perspective and understanding of Keynes’s 1933 lecture by not solely reading or relying upon the text of the three contextualised essay versions of his lecture. The purpose and context of Keynes’s original longer lecture version was quite different to the three shorter essay versions published for the American, British and German audiences.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Exploring an agent as an economic insider threat solution

The insider threat is a security problem that is well-known and has a long history, yet it still remains an invisible enemy. Insiders know the security processes and have accesses that allow them to easily cover their tracks. In recent years the idea of monitoring separately for these threats has come into its own. However, the tools currently in use have disadvantages and one of the most effective techniques of human review is costly. This paper explores the development of an intelligent agent that uses already in-place computing material for inference as an inexpensive monitoring tool for insider threats. Design Science Research (DSR) is a methodology used to explore and develop an IT artifact, such as for this intelligent agent research. This methodology allows for a structure that can guide a deep search method for problems that may not be possible to solve or could add to a phenomenological instantiation.

Depression and 'crises of meaninglessness' in the political-economic theology of the money-God

The thesis starts with a historical analysis of the development of depression as a concept. Through this inquiry, the controversies behind the apparent consensus about depression’s etiology and treatment are illuminated, suggesting that the understanding of the climbing rates of depression in contemporary Western civilization is still up for grabs. That’s what the thesis sets out to investigate. In order to accomplish this aim, the study builds upon the classical accounts of Georg Simmel, Émile Durkheim and the more contemporary ideas of Dany-Robert Dufour, in dialogue with an array of supplementary theoretical sources. Navigating through this ‘sea’ of extraordinary and different theories, a new avenue of reflections arises, contributing for the sophistication of the questions made about the phenomenon of depression’s rates. The fundamental argument emerging from this theoretical undertaking is that ‘crises of meaninglessness’ that pervade the collective body of Western contemporary societies have, as one of its consequences, the expansion of depression rates. Meaninglessness in contemporary times is the primary object of investigation of the thesis. The concept, in the context of this study, is not understood as merely an effect of the historical decline of shared social norms due to processes of individualization. Rather, it is claimed, it originates from and is reinforced by the ‘political-economic theology of neo-liberalism’ which becomes virtually generalized in the West, erecting money as a God. The study concludes that by undermining culturally established values, ideals, institutions and principles that may block the dissemination of commodities this new transcendence has been challenging the task of signifying life, potentializing – among other subjective difficulties – the diffusion of depression.