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.

The corporate social responsibility movement and law's empire: Is there a conflict?

At the heart of corporate governance and social responsibility discourse is recognition of the fact that the modern corporation is primarily governed by the profit maximisation imperative coupled with moral and ethical concerns that such a limited imperative drives the actions of large and wealthy corporations which have the ability to act in influential and significant ways, shaping how our social world is experienced. The actions of the corporation and its management will have a wide sphere of impact over all of its stakeholders whether these are employees, shareholders, consumers or the community in which the corporation is located. As globalisation has become central to the way we think it is also clear that ‘community’ has an ever expanding meaning which may include workers and communities living very far away from Corporate HQ. In recent years academic commentators have become increasingly concerned about the emphasis on what can be called short-term profit maximisation and the perception that this extremist interpretation of the profit imperative results in morally and ethically unacceptable outcomes.1 Hence demands for more corporate social responsibility. Following Cadbury’s2 classification of corporate social responsibility into three distinct areas, this paper will argue that once the legally regulated tier is left aside corporate responsibility can become so nebulous as to be relatively meaningless. The argument is not that corporations should not be required to act in socially responsible ways but that unless supported by regulation, which either demands high standards, or at the very least incentivises the attainment of such standards such initiatives are doomed to failure. The paper will illustrate by reference to various chosen cases that law’s discourse has already signposted ways to consider and resolve corporate governance problems in the broader social responsibility context.3 It will also illustrate how corporate responsibility can and must be supported by legal measures. Secondly, this paper will consider the potential conflict between an emphasis on corporate social responsibility and the regulatory approach.4 Finally, this paper will place the current interest in corporate social responsibility within the broader debate on the relationship between law and non-legally enforceable norms and will present some reflections on the norm debate arising from this consideration of the CSR movement.

Initial losses, corporate governance and earnings management

This thesis examines the relationship between initial loss events and the corporate governance and earnings management behaviour of these firms. This is done using four years of corporate governance information spanning the report of an initial loss for companies listed on the UK Stock Exchange. An industry- and sizematched control sample is used in a difference-in-difference analysis to isolate the impact of the initial loss event during the period. It is reported that, in general, an initial loss motivates an improvement in corporate governance in those loss firms where a relative weakness existed prior to the loss and that these changes mainly occur before the initial loss is announced. Firms with stronger (i.e. better quality) corporate governance have less need to alter it in response to the loss. It is also reported that initial loss firms use positive abnormal accruals in the year before the loss in an attempt to defer/avoid the loss — the weaker corporate governance the more likely is it that loss firms manage earnings in this manner. Abnormal accruals are also found to be predictive of an initial loss and when used as a conditioning variable, the quality of corporate governance is an important mitigating factor in this regard. Once the loss is reported, loss firms unwind these abnormal accruals although no evidence of big-bath behaviour is found. The extent to which these abnormal accruals are subsequently unwound are also found to be a function of both the quality of corporate governance as well as the severity of the initial loss.

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.

Design history: exploring corporate communities

A design history is a narrative involving a multitude of social groups, interpretive flexibility, and eventual stabilization of shared understanding. Design history surfaces the practices that help shape and define engagements and can increase not only our theoretical understanding of what design is, but also our capacity to realize this understanding in practice. We use a design history perspective to examine how corporate technology initiatives establish and support open source communities and the crafting of relevant design practices that enable their advancement. We foster an evolving expression of design research that treats artifacts not as stable objects to be singularly evaluated, but as evolving systems contingent on historical trajectories.