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.

The Resurgence of Gaelic power in Ireland and Scotland and its wider impact, c.1350-1513

Over the course of the later Middle Ages nearly half the landmass of the British Isles fell under the control of a handful of Gaelic dynasties in Ireland and Scotland. The impact of this profound geopolitical recasting of much of the Atlantic Archipelago has however, received very little scholarly attention. Instead, historians have tended to view events within this expanding Gaelic world, or Gàidhealtachd, as peripheral to the political development of the British Isles and the course of Anglo-Scottish relations during the later Medieval period. Drawing upon a comprehensive range of sources from Ireland, Scotland, and England, as well as significant archival research, this thesis challenges the concept of the so-called 'Celtic fringe' and illustrates how developments within the Gàidhealtachd impacted upon the course of 'British' politics during the period c.1350-1513. The thesis centres on an examination of how two competing Gaelic alliance systems came to dominate much of the Gàidhealtachd from the late fourteenth century through to the early 1500s. The first of these alliance blocs was controlled chiefly by the O'Neills of Tyrone, the O'Briens of Thomond, and MacDonalds of the Hebrides; in the other network the O'Donnells of Tyrconnell, the Burkes of Mayo and the Campbells of Argyll held sway. By tracing the interconnectivity of the lordships in each respective network, the thesis investigates how these alliance systems became a durable force not only within the Gàidhealtachd but also on the broader 'British' stage. The thesis is structured in a manner that makes the intricate, lineage-based world of the Gàidhealtachd more accessible. Each chapter shifts between the various regions of the Gaelic world and examines how developments in one region impacted upon corresponding territory. Ultimately, this provides historians with a new model for exploring what has previously been a majorly neglected area of Irish and British history.

Practical assessment of hardware limitations on power aware wireless sensor networks - An anti-windup approach

This work considers the effect of hardware constraints that typically arise in practical power-aware wireless sensor network systems. A rigorous methodology is presented that quantifies the effect of output power limit and quantization constraints on bit error rate performance. The approach uses a novel, intuitively appealing means of addressing the output power constraint, wherein the attendant saturation block is mapped from the output of the plant to its input and compensation is then achieved using a robust anti-windup scheme. A priori levels of system performance are attained using a quantitative feedback theory approach on the initial, linear stage of the design paradigm. This hybrid design is assessed experimentally using a fully compliant 802.15.4 testbed where mobility is introduced through the use of autonomous robots. A benchmark comparison between the new approach and a number of existing strategies is also presented.

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.