Meteorologists working in numerical weather prediction are faced with two distinct types of problems. The first problem is to devise a mathematical model which will represent the significant physical processes that occur in the atmosphere. The second problem is to integrate the model equations by applying numerical techniques over a grid network ot discrete points. [...]

This dissertation has three main parts. In the first part, the construct of psychopathy is described, its theoretical relevance for predicting recidivism is examined, and the literature on The Hare Psychopathy Checklist-Revised (PCL-R; Hare, 1980, 1991) and recidivism is briefly reviewed. The association between psychopathy and recidivism (general, violent) was examined in five samples (N > 800 inmates) of provincial and federal male inmates who were incarcerated in British Columbia between 1964 and 1995. Results were consistent across samples and across measures and indicated that psychopathy was positively associated with recidivism. These findings indicate that psychopathy is important for identifying inmates who are at risk to be reconvicted. In the second part of the dissertation, a comprehensive and empirically-based set of crime categories was developed. Crimes were sorted into 200 descriptive categories and then collapsed into broader categories using frequency counts and factor analysis. Results indicated that the four most frequently occurring crime categories (break and enter, fraud, theft, possession of illegal property) accounted for more than half of all convictions, whereas the remaining 25 crime categories accountedfor less than half of all convictions. In the third part of the dissertation, PCL-R scores, frequency counts for the crime categories, and basic demographic variables, were entered into a stepwise discriminant function analysis to predict general recidivism (yes, no) and into another discriminant function analysis to predict violent recidivism. The percentage of general recidivists who were correctly classified (81.3%) was similar in magnitude to the base rate of general recidivism (81.1%). In terms of violent recidivism, five variables (PCL-R scores, two age variables, previous convictions for robbery and for assault) emerged as important predictors. Scores on each of these five predictors were assigned weights, and the weights were summed together to form a violence risk score. Higher scores on the violence risk scale identified inmates who were at higher risk to be convicted of violent recidivism. Scores on the risk instrument correctly classified 62.2% of inmates into violent (yes, no) recidivism groups. These results held-up under cross-validation; in an independent sample of 124 inmates, 64.5% of inmates were correctly classified. The findings indicate that the violence risk scale has promise as a measure for identifying inmates who are at risk to be convicted of future violence.

The demand for solar radiation forecasting has become a significant feature in the design of photovoltaic (PV) systems. Currently, the artificial neural network (ANN) is the most popular model that is used to estimate solar radiation. However, a new approach, called the extreme learning machine (ELM) algorithm, has been introduced by Huang et al. In this research, ELM and a multilayer feed-forward network with back propagation were used to predict daily global solar radiation. Metrological parameters such as air temperature, humidity and date code have been used as inputs for the ANN and ELM models. The accuracy and performance of these techniques were evaluated by comparing their outputs. ELM is faster than ANN, and results in a high generalization capability. / It is a comperison between ANN and ELM

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In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model.