This thesis reports findings from a number of modern machine learning techniques applied to electricity market price forecasting. The techniques evaluated were Support Vector Machines, Boosting, Bayesian networks, neural networks and a weekly average method. All techniques were evaluated on seven day into the future forecasting of the Regional Reference (pool) Prices (RRP) for the New South Wales (NSW) region of the Australian National Electricity Market (NEM). Due to highly volatile and non-repetitive nature of the NSW RRP, all complex machine learning methods provided inferior accuracy forecasts compared to a weekly average method. The weekly average method was computationally less expensive and more transparent to the user than any of the machine learning techniques. The Support Vector Machine (SVM) was chosen for its novel application to electricity price forecasting because it is considered to be the next generation to neural networks. The structured SVM training algorithm proved more consistent and reliable than the neural network algorithm. Bayesian networks offer the adaptability of a neural network with the advantage of providing a price forecast with confidence intervals for each half-hour determined from the actual data. The SVM and Bayesian techniques were found to provide acceptable forecasts for NSW demand. An investigation of international electricity markets found that each market was unique with different market structures, regulations, network topologies and ownership regimes. Price forecasting techniques and results cannot be universally applied without careful consideration of local conditions. For instance, price data for the Spanish and Californian electricity markets were investigated and found to have significantly lower price volatility than the NSW region of the NEM. An extensive examination of the NSW RRP showed that the price exhibited no consistent long-term trend. A stationary data set could not be extracted from the price data. Thus, making forecasting unsuited to techniques using large historical data sets. The strongest pattern found for NSW prices was the weekly cycle, so a weekly average method was developed to utilise this weekly cycle. Over 25 weeks of NSW RRP from February to July 2002, the seven day into the future price forecast mean absolute error (MAE) for the SVM technique was 27.8%. The weekly average method was more accurate with an MAE of 20.6% and with a simple linear price adjustment for demand, the error was reduced to 18.1%. The price spikes and uneven distribution of prices were unsuitable for the Boosting or Bayesian network techniques.

Electrified fencing is commonly used throughout the world to control animals with smaller and cheaper fence constructions than would otherwise be necessary with non-electrified wires. Typical installations have a long wire or wires starting from an electric fence energiser and then surrounding fields in various complex configurations. Faults on electric fences can be difficult to locate, with the average fence using tens of kilometres of wire. Basic fault-finding tools allow an operator to read the peak fence voltage, requiring the user to decide whether a fault is present and to randomly search for the source of the problem. The focus of this thesis is to develop a device that reduces the time to locate faults on a fence by providing more information about the location and nature of a fault, and will point in the direction of the fault.

Whole document restricted, see Access Instructions file below for details of how to access the print copy. / Future telecommunication networks will employ digital transmission techniques. Such networks will provide a number of benefits including the ability to integrate voice and non-voice messages. The transmission channel of this network can be represented by a cascaded channel composed of a number of elementary channels connected in series. Therefore the modelling of such a channel is of particular interest. The influence of noise and other impairments in the cascaded binary channel cause errors which may be represented by a binary signal called the error sequence. Consequently, an important step in digital channel modelling is estimation of parameters and distribution functions which characterise the statistical properties of error sequences in the channel. Thus, the development of efficient methods for this estimation is a problem of long term interest which should be properly solved. This thesis presents methods and techniques for parameter (primarily the probability of error) and distribution function (primarily the error gap complementary distribution function) estimation using the error sequences obtained by measurement or simulation in elementary or cascaded channels. Theoretical analysis and testing confirm that it is possible to control the accuracy and reliability of estimation. Two principal and practical methods for the probability of error estimation are developed: the modified Monte Carlo method (MMC); and the method based on Chebyshev inequality (MCI). In contrast to the traditional Monte Carlo method based on classical statistics, the methods developed in this thesis aim to specify the sample size required to achieve the desired accuracy. The methods developed are based on the dependence of the sample size on the estimated value of a parameter being estimated. Hence the sample size is a random variable and the confidence limits factor (which specifies the width of confidence interval in respect to the estimated value) is a constant. Based on these methods, this thesis proposes and demonstrates two techniques for parameter estimation. The traditional Monte Carlo method has been primarily used for the probability of error estimation in channels without memory. In this thesis the capabilities of this method are extended to the case of estimating the probability of error in channels with memory and cascaded channels. However, even with this extension, this method is not practical due to its complexity and limitations on the qualification and quantification of the accuracy and reliability of estimation. Also, the extended method is unable to satisfactorily estimate the probability of error in cascaded channels with memory; nor could it improve the speed of the estimation process. Two methods and two techniques for distribution function estimation are developed in this thesis. They are demonstrated by estimating the error gap complementary functions of simulated data. For this purpose, simulators of binary channels with and without memory have been developed. The methods and techniques are characterised by their simplicity in application; ability to quantify the accuracy and reliability; time efficiency; and real time capability. The wider application of the methods and techniques developed in this thesis are demonstrated on three examples: a distribution function estimation using data obtained by indoor wideband radio propagation measurement; BER characteristics measurement; and measurement of the residual probability of error in transmission systems using error correcting codes. From the results obtained in the thesis some recommendations for future work in the field of digital channel modelling and simulation are discussed.

This thesis describes a novel approach for adaptive optimal control and demonstrates its application to a variety of systems, including motion control learning for legged robots. The new controller, called “FOX”, uses a modified form of Albus’s CMAC neural network. It is trained to generate control signals that minimize a system’s performance error. A theoretical consideration of the adaptive control problem is used to show that FOX must assign each CMAC weight an “eligibility” value which controls how that weight is updated. FOX thus implements a kind of reinforcement learning which makes it functionally similar to the cerebellum (a part of the brain that modulates movement). A highly efficient implementation is described which makes FOX suitable for on-line control. FOX requires a small amount of dynamical information about the system being controlled: the system’s impulse response is used to choose the rules that update the eligibility values. A FOX-based controller design methodology is developed, and FOX is tested on four control problems: controlling a simulated linear system, controlling a model gantry crane, balancing an inverted pendulum on a cart, and making a wheeled robot follow a path. In each case FOX is effective: it associates sensor values with (and anticipates) the correct control actions, it compensates for system nonlinearities, and it provides robust control as long as the training is comprehensive enough. FOX is also applied to the control of a simulated hopping monoped, and a walking biped. FOX learns parameters that fine tune the movements of pre-programmed controllers, in a manner analogous to the cerebellar modulation of spinal cord reflexes in human movement. The robots are successfully taught how to move with a steady gait along flat ground, in any direction, and how to climb and descend slopes.

In February 1965, a research programme in radio astronomy was started by the Electrical Engineering Department of the University of Auckland. One of the main purposes of the programme was to provide a unified theme for post-graduate research in the department. The initial aim of the programme was to develop through graduate research, sufficient facilities for radio astronomy observations at frequencies below 100Mhz. Being among the first group of graduate students in the programme, the author was given the problem of studying the antenna requirements for the programme. At the frequencies concerned, the antenna systems are often large and expensive. As the programme is supported at present, only by funds for ordinary graduate research, there is a great need for an antenna array with good performance at minimal cost. This has led the author into his main field of study, viz. the synthesis of arrays with non-uniformly spaced directional elements. The use of directional elements together with non-uniform spacing technique permits larger inter-element spacings in the array without resulting in large sidelobes in the response pattern. Available synthesis methods are inadequate despite the large number of papers published on the subject since its introduction 7 years ago. The synthesis problem involves the determination of a set of element positions to give a desirable response pattern. Because the element position variables lie in the arguments of the cosine terms in the pattern function, the problem becomes highly non-linear. For simplicity, most of the published works have assumed isotropic elements. The methods proposed have been mainly centred on some form of linear approximations to the problem. Consequently, these methods are only effective over a limited region of space*. As a result of this limitation, good pattern characteristics can only be achieved with impractically small spacings. For arrays with less than about 50 elements, the element spacings can best be determined by an optimisation procedure. This method involves the repeated application of small pertubations to the element positions of a starting array until maximum improvement to the sidelobe levels of the array is achieved. An efficient perturbation method has been proposed by Baklanov et al using a matrix approach. Because of the inherent limitation of this method, Baklanov’s arrays are mostly impractical due to the occurrence of small spacings. Such limitations are removed by the author through the use of a modified synthesis procedure. With this new procedure, the author was able to control the pattern over a considerably larger area in space. Thus arrays with average inter-element spacings up to two wavelengths can be synthesised with positive control over all sidelobes in the arrays. The sidelobe levels of the author’s arrays are, as a whole, bery close to the levels of the corresponding theoretically optimal patterns. The element directivities are taken into account in the synthesis process. A total of 30 non-uniformly spaced arrays of varying sizes and sidelobe levels were synthesised using the method developed. Since all these arrays have near to optimal sidelobe characteristics, they provide a basis for a detailed study of the properties of non-uniformly spaced arrays as a whole. A number of interesting points are revealed when pattern parameters like gain, beamwidth, sidelobe level, etc., are studied in relation with the spacing characteristics of the arrays. A better understanding of the properties of non-uniformly spaced arrays is also gained by comparing the pattern characteristics of the synthesised arrays with that of current tapered arrays. The design and testing of a 16-element non-uniformly spaced array of Yagi antennas is described in Chapter 4. This array demonstrates one practical application of the synthesis work reported in this thesis. *The word ‘space’ used throughout the Introduction does not mean the physical space, which is defined, in a 2-D case, by the zenith angle θ. Here, the space is defined by the parameter χ=2πdavsinθ/λ. Thus with an average inter-element spacing dav= λ, the visible space is defined by χ=0 to π.

Whole document restricted, see Access Instructions file below for details of how to access the print copy. / Future telecommunication networks will employ digital transmission techniques. Such networks will provide a number of benefits including the ability to integrate voice and non-voice messages. The transmission channel of this network can be represented by a cascaded channel composed of a number of elementary channels connected in series. Therefore the modelling of such a channel is of particular interest. The influence of noise and other impairments in the cascaded binary channel cause errors which may be represented by a binary signal called the error sequence. Consequently, an important step in digital channel modelling is estimation of parameters and distribution functions which characterise the statistical properties of error sequences in the channel. Thus, the development of efficient methods for this estimation is a problem of long term interest which should be properly solved. This thesis presents methods and techniques for parameter (primarily the probability of error) and distribution function (primarily the error gap complementary distribution function) estimation using the error sequences obtained by measurement or simulation in elementary or cascaded channels. Theoretical analysis and testing confirm that it is possible to control the accuracy and reliability of estimation. Two principal and practical methods for the probability of error estimation are developed: the modified Monte Carlo method (MMC); and the method based on Chebyshev inequality (MCI). In contrast to the traditional Monte Carlo method based on classical statistics, the methods developed in this thesis aim to specify the sample size required to achieve the desired accuracy. The methods developed are based on the dependence of the sample size on the estimated value of a parameter being estimated. Hence the sample size is a random variable and the confidence limits factor (which specifies the width of confidence interval in respect to the estimated value) is a constant. Based on these methods, this thesis proposes and demonstrates two techniques for parameter estimation. The traditional Monte Carlo method has been primarily used for the probability of error estimation in channels without memory. In this thesis the capabilities of this method are extended to the case of estimating the probability of error in channels with memory and cascaded channels. However, even with this extension, this method is not practical due to its complexity and limitations on the qualification and quantification of the accuracy and reliability of estimation. Also, the extended method is unable to satisfactorily estimate the probability of error in cascaded channels with memory; nor could it improve the speed of the estimation process. Two methods and two techniques for distribution function estimation are developed in this thesis. They are demonstrated by estimating the error gap complementary functions of simulated data. For this purpose, simulators of binary channels with and without memory have been developed. The methods and techniques are characterised by their simplicity in application; ability to quantify the accuracy and reliability; time efficiency; and real time capability. The wider application of the methods and techniques developed in this thesis are demonstrated on three examples: a distribution function estimation using data obtained by indoor wideband radio propagation measurement; BER characteristics measurement; and measurement of the residual probability of error in transmission systems using error correcting codes. From the results obtained in the thesis some recommendations for future work in the field of digital channel modelling and simulation are discussed.

This thesis describes a novel approach for adaptive optimal control and demonstrates its application to a variety of systems, including motion control learning for legged robots. The new controller, called “FOX”, uses a modified form of Albus’s CMAC neural network. It is trained to generate control signals that minimize a system’s performance error. A theoretical consideration of the adaptive control problem is used to show that FOX must assign each CMAC weight an “eligibility” value which controls how that weight is updated. FOX thus implements a kind of reinforcement learning which makes it functionally similar to the cerebellum (a part of the brain that modulates movement). A highly efficient implementation is described which makes FOX suitable for on-line control. FOX requires a small amount of dynamical information about the system being controlled: the system’s impulse response is used to choose the rules that update the eligibility values. A FOX-based controller design methodology is developed, and FOX is tested on four control problems: controlling a simulated linear system, controlling a model gantry crane, balancing an inverted pendulum on a cart, and making a wheeled robot follow a path. In each case FOX is effective: it associates sensor values with (and anticipates) the correct control actions, it compensates for system nonlinearities, and it provides robust control as long as the training is comprehensive enough. FOX is also applied to the control of a simulated hopping monoped, and a walking biped. FOX learns parameters that fine tune the movements of pre-programmed controllers, in a manner analogous to the cerebellar modulation of spinal cord reflexes in human movement. The robots are successfully taught how to move with a steady gait along flat ground, in any direction, and how to climb and descend slopes.

In February 1965, a research programme in radio astronomy was started by the Electrical Engineering Department of the University of Auckland. One of the main purposes of the programme was to provide a unified theme for post-graduate research in the department. The initial aim of the programme was to develop through graduate research, sufficient facilities for radio astronomy observations at frequencies below 100Mhz. Being among the first group of graduate students in the programme, the author was given the problem of studying the antenna requirements for the programme. At the frequencies concerned, the antenna systems are often large and expensive. As the programme is supported at present, only by funds for ordinary graduate research, there is a great need for an antenna array with good performance at minimal cost. This has led the author into his main field of study, viz. the synthesis of arrays with non-uniformly spaced directional elements. The use of directional elements together with non-uniform spacing technique permits larger inter-element spacings in the array without resulting in large sidelobes in the response pattern. Available synthesis methods are inadequate despite the large number of papers published on the subject since its introduction 7 years ago. The synthesis problem involves the determination of a set of element positions to give a desirable response pattern. Because the element position variables lie in the arguments of the cosine terms in the pattern function, the problem becomes highly non-linear. For simplicity, most of the published works have assumed isotropic elements. The methods proposed have been mainly centred on some form of linear approximations to the problem. Consequently, these methods are only effective over a limited region of space*. As a result of this limitation, good pattern characteristics can only be achieved with impractically small spacings. For arrays with less than about 50 elements, the element spacings can best be determined by an optimisation procedure. This method involves the repeated application of small pertubations to the element positions of a starting array until maximum improvement to the sidelobe levels of the array is achieved. An efficient perturbation method has been proposed by Baklanov et al using a matrix approach. Because of the inherent limitation of this method, Baklanov’s arrays are mostly impractical due to the occurrence of small spacings. Such limitations are removed by the author through the use of a modified synthesis procedure. With this new procedure, the author was able to control the pattern over a considerably larger area in space. Thus arrays with average inter-element spacings up to two wavelengths can be synthesised with positive control over all sidelobes in the arrays. The sidelobe levels of the author’s arrays are, as a whole, bery close to the levels of the corresponding theoretically optimal patterns. The element directivities are taken into account in the synthesis process. A total of 30 non-uniformly spaced arrays of varying sizes and sidelobe levels were synthesised using the method developed. Since all these arrays have near to optimal sidelobe characteristics, they provide a basis for a detailed study of the properties of non-uniformly spaced arrays as a whole. A number of interesting points are revealed when pattern parameters like gain, beamwidth, sidelobe level, etc., are studied in relation with the spacing characteristics of the arrays. A better understanding of the properties of non-uniformly spaced arrays is also gained by comparing the pattern characteristics of the synthesised arrays with that of current tapered arrays. The design and testing of a 16-element non-uniformly spaced array of Yagi antennas is described in Chapter 4. This array demonstrates one practical application of the synthesis work reported in this thesis. *The word ‘space’ used throughout the Introduction does not mean the physical space, which is defined, in a 2-D case, by the zenith angle θ. Here, the space is defined by the parameter χ=2πdavsinθ/λ. Thus with an average inter-element spacing dav= λ, the visible space is defined by χ=0 to π.

The deregulation of the electricity supply industry in New Zealand has led to an increased level of interest in the security of electricity supply to rural communities. This in turn has led to questions about sustainable alternatives to conventional methods of electricity supply. A solution may be the adoption of sustainable community sized renewable energy (RE) based distributed generation systems. However, choosing between the myriad of possibilities requires much data and analysis. An accurate analysis of electricity load and RE resource matching is normally required. In most cases, this is an expensive and time-consuming assessment. In order to minimise these costs, and yet give due consideration to stakeholder preferences and technical uncertainty, a process incorporating the economic, social, environmental, and technical aspects of sustainable design in a relatively short timeframe will be required. This study developed such a method through the integrated use of the wind atlas assessment and analysis program (WAsP), the micropower optimisation model (HOMER), and three methods of decision analysis using Logical Decisions for Windows (LDW) software, which formed the decision analysis framework, SPiRAL (Sustainable Power in Rural Areas and Locations). The efficacy of the integrated use of the software in the SPiRAL framework was tested through two analyses using electricity load and RE resource data from a case study site. The first was an analysis using a full-year of data in a multi-method decision analysis process thus setting the framework in place. A further analysis then tested the minimum monitoring time required to obtain and analyse the data for modelling meaningful results. In both analyses, the results were ranked based on stakeholder preferences between the economic, social, environmental, and technical aspects of sustainable energy systems. The clear representation of the uncertainty of the electricity loads and the RE resources was paramount in the results. The short-term analysis results differed in small ways from the full-term, but were essentially similar. This study developed a decision analysis framework that delivered transparent results in a manner likely to instil insight and confidence in them, and this would provide the decision-maker with much valuable information on which to base their decision.

Whole document restricted, see Access Instructions file below for details of how to access the print copy. / Future telecommunication networks will employ digital transmission techniques. Such networks will provide a number of benefits including the ability to integrate voice and non-voice messages. The transmission channel of this network can be represented by a cascaded channel composed of a number of elementary channels connected in series. Therefore the modelling of such a channel is of particular interest. The influence of noise and other impairments in the cascaded binary channel cause errors which may be represented by a binary signal called the error sequence. Consequently, an important step in digital channel modelling is estimation of parameters and distribution functions which characterise the statistical properties of error sequences in the channel. Thus, the development of efficient methods for this estimation is a problem of long term interest which should be properly solved. This thesis presents methods and techniques for parameter (primarily the probability of error) and distribution function (primarily the error gap complementary distribution function) estimation using the error sequences obtained by measurement or simulation in elementary or cascaded channels. Theoretical analysis and testing confirm that it is possible to control the accuracy and reliability of estimation. Two principal and practical methods for the probability of error estimation are developed: the modified Monte Carlo method (MMC); and the method based on Chebyshev inequality (MCI). In contrast to the traditional Monte Carlo method based on classical statistics, the methods developed in this thesis aim to specify the sample size required to achieve the desired accuracy. The methods developed are based on the dependence of the sample size on the estimated value of a parameter being estimated. Hence the sample size is a random variable and the confidence limits factor (which specifies the width of confidence interval in respect to the estimated value) is a constant. Based on these methods, this thesis proposes and demonstrates two techniques for parameter estimation. The traditional Monte Carlo method has been primarily used for the probability of error estimation in channels without memory. In this thesis the capabilities of this method are extended to the case of estimating the probability of error in channels with memory and cascaded channels. However, even with this extension, this method is not practical due to its complexity and limitations on the qualification and quantification of the accuracy and reliability of estimation. Also, the extended method is unable to satisfactorily estimate the probability of error in cascaded channels with memory; nor could it improve the speed of the estimation process. Two methods and two techniques for distribution function estimation are developed in this thesis. They are demonstrated by estimating the error gap complementary functions of simulated data. For this purpose, simulators of binary channels with and without memory have been developed. The methods and techniques are characterised by their simplicity in application; ability to quantify the accuracy and reliability; time efficiency; and real time capability. The wider application of the methods and techniques developed in this thesis are demonstrated on three examples: a distribution function estimation using data obtained by indoor wideband radio propagation measurement; BER characteristics measurement; and measurement of the residual probability of error in transmission systems using error correcting codes. From the results obtained in the thesis some recommendations for future work in the field of digital channel modelling and simulation are discussed.