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.

Farmers, agriculture and politics in the Irish Free State area, 1919-1936

Given the economic and social importance of agriculture in the early years of the Irish Free State, it is surprising that the development of organisations representing farmers has not received the attention it deserves from historians. While the issues of government agricultural policy and the land question have been extensively studied in the historiography, the autonomous response by farmers to agricultural policies and the detailed study of the farmers’ organisations has simply been ignored in spite of the existence of a range of relevant primary sources. Farmers’ organisations have only received cursory treatment in these studies; they have been presented as passive spectators, responding in a Pavlovian manner to outside events. The existing historiography has only studied farmers’ organisations during periods when they impinged on national politics, epecially during the War of Independence and the Economic War. Therefore chronological gaps exist which has led to much misinterpretation of farmers’ activities. This thesis will redress this imbalance by studying the formation and continuous development of farmers’ organisations within the twenty-six county area and the reaction of farmers to changing government agricultural policies, over the period 1919 to 1936. The period under review entailed many attempts by farmers to form representative organisations and encompassed differing policy regimes. The thesis will open in 1919, when the first national organisation representing farmers, the Irish Farmers’ Union, was formed. In 1922, the union established the Farmers’ Party. By the mid- 1920’s, a number of protectionist agricultural associations had been formed. While the Farmers’ Party was eventually absorbed by Cumann na nGaedheal, local associations of independent farmers occupied the resultant vacuum and contested the 1932 election. These organisations formed the nucleus of a new national organisation; the National Farmers’ and Ratepayers’ League. The agricultural crisis caused by both the Great Depression and the Economic War facilitated the expansion of the league. The league formed a political party, the Centre Party, to contest the 1933 election. While the Centre Party was absorbed by the newly-formed Fine Gael, activists from the former farmer organisations led the campaign against the payment of annuities and rates. Many of them continued this campaign after 1934, when the Fine Gael leadership opposed the violent resistance to the collection of annuities. New farmer organisations were formed to co-ordinate this campaign which continued until 1936, the closing point of the thesis.

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.