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Short-term load forecasting (STLF) is an important part of the power generation process. For years, it has been achieved by traditional approaches stochastic like time series / but, new methods based on artificial intelligence emerged recently in literature and started to replace the old ones in the industry. In order to follow the latest developments and to have a modern system, it is aimed to make a research on STLF in Turkey, by neural networks. For this purpose, a method is proposed to forecast Turkey&rsquo / s total electric load one day in advance. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to make use of the available past data for adapting the weights and to further adjust these connections according to the changing conditions. It is also suggested to tune the step size iteratively for better accuracy. Since a single neural network model cannot cover all load types, data are clustered due to the differences in their characteristics. Apart from this, special days are extracted from the normal training sets and handled separately. In this way, a solution is proposed for all load types, including working days, weekends and special holidays. For the selection of input parameters, a technique based on principal component analysis is suggested. A traditional ARMA model is constructed for the same data as a benchmark and results are compared. Proposed method gives lower percent errors all the time, especially for holiday loads. The average error for year 2002 data is obtained as 1.60%.

In a global economy and a world of increasing polarisation and unsustainable development, learning is critical to change. With most of the world's population in cities and the region increasingly the focus of measures to improve prospects, the learning region concept integrates the conflicting, diverse and complex issues of development. This thesis examines learning in networks and further develops the theory of the learning region through a case study in Melbourne, Australia. It begins with a case study of an industry network which was part of a research project - City Regions, Intelligent Territories, Innovation Competitiveness and Learning (CRITICAL). The CRITICAL research project examined learning processes in five cities and developed theory and tools to support learning regions. In this thesis the study of the industry network became the first step in a case study of the northern metropolitan regional economic development project. The study of the region demonstrated that there existed a strategic regional approach supporting local learning and action developed through projects based on local research and collaboration. The theories of 'communities of practice and 'architectures of learning' (Wenger 1998) provided the conceptual framework for the case study and proved to be a novel way to discern how learning was supported. A key finding in this thesis was how learning in networks was supported and that this led to organisational change, innovation and learning across differ ent sectors and organisations. Data was analysed using a typology of the learning region developed in the CRITICAL project and the region was found to have characteristics of a learning region although without wider connectivity across the city could only be considered a sub region. The study contributes to the body of work which demonstrates that the university can play a significant role in supporting the learning region and local engagement of key organisations, enterprises and government, and in the understanding of policy and programs to develop learning regions. The findings also contribute to innovation theory particularly with regard to networks and small to medium enterprises in manufacturing. Findings support the development of frameworks for urban regional development with the partnering of different levels of government to create new ways of operating and learning in the emerging mode of local governance partnerships and highlight the need to develop ways of measuring and understanding success or failure which capture the social, economic, cultural and environmental priorities of society.

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 168-185).

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 136-147). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph.D.) OGI School of Science & Engineering at OHSU, January 2007. / Abstract: leaves xiii-xiv. Includes bibliographical references (leaves 137-153).

The thesis underpinning this study is as follows; it is possible to build machine models that are indistinguishable from the mental models used by humans to generate language to describe their environment. This is to say that the machine model should perform in such a way that a human listener could not discern whether a description of a scene was generated by a human or by the machine model. Many linguistic processes are used to generate even simple scene descriptions and developing machine models of all of them is beyond the scope of this study. The goal of this study is, therefore, to model a sufficient part of the scene description process, operating in a sufficiently realistic environment, so that the likelihood of being able to build machine models of the remaining processes, operating in the real world, can be established. The relatively under-researched process of reference object selection is chosen as the focus of this study. A reference object is, for instance, the `table' in the phrase ``The flowers are on the table''. This study demonstrates that the reference selection process is of similar complexity to others involved in generating scene descriptions which include: assigning prepositions, selecting reference frames and disambiguating objects (usually termed `generating referring expressions'). The secondary thesis of this study is therefore; it is possible to build a machine model that is indistinguishable from the mental models used by humans in selecting reference objects. Most of the practical work in the study is aimed at establishing this. An environment sufficiently near to the real-world for the machine models to operate on is developed as part of this study. It consists of a series of 3-dimensional scenes containing multiple objects that are recognisable to humans and `readable' by the machine models. The rationale for this approach is discussed. The performance of human subjects in describing this environment is evaluated, and measures by which the human performance can be compared to the performance of the machine models are discussed. The machine models used in the study are variants on Bayesian networks. A new approach to learning the structure of a subset of Bayesian networks is presented. Simple existing Bayesian classifiers such as naive or tree augmented naive networks did not perform sufficiently well. A significant result of this study is that useful machine models for reference object choice are of such complexity that a machine learning approach is required. Earlier proposals based on sum-of weighted-factors or similar constructions will not produce satisfactory models. Two differently derived sets of variables are used and compared in this study. Firstly variables derived from the basic geometry of the scene and the properties of objects are used. Models built from these variables match the choice of reference of a group of humans some 73\% of the time, as compared with 90\% for the median human subject. Secondly variables derived from `ray casting' the scene are used. Ray cast variables performed much worse than anticipated, suggesting that humans use object knowledge as well as immediate perception in the reference choice task. Models combining geometric and ray-cast variables match the choice of reference of the group of humans some 76\% of the time. Although niether of these machine models are likely to be indistinguishable from a human, the reference choices are rarely, if ever, entirely ridiculous. A secondary goal of the study is to contribute to the understanding of the process by which humans select reference objects. Several statistically significant results concerning the necessary complexity of the human models and the nature of the variables within them are established. Problems that remain with both the representation of the near-real-world environment and the Bayesian models and variables used within them are detailed. While these problems cast some doubt on the results it is argued that solving these problems is possible and would, on balance, lead to improved performance of the machine models. This further supports the assertion that machine models producing reference choices indistinguishable from those of humans are possible.

Thesis (Ph.D.)--Utrecht University, 2006. / Includes bibliographical references (p. [133]-137).