The number of solar panels deployed worldwide has rapidly increased. Solar panels are often placed in areas not easily accessible. It is also difficult for panel owners to be aware of their operating condition. Many environmental factors have negative effects on the efficiency of solar panels. To reduce the power lost caused by environmental factors, it is necessary to detect and classify the anomalous events occurring on the surface of solar panels. This thesis designs and studies a device to continuously measure the voltage output of solar panels and to transmit the time series data back to a personal computer using wireless communication. A program was developed to store and model this time series data. It also detected the existence of anomalies and classified the anomalies by modeling the data. In total, ten types of anomalies were considered. These anomaly types include temporary shading, permanent shading, fallen leaves, accumulating snow and melting snow among others. Previous time series anomaly detection algorithms do not perform well for reallife situations and are only capable of dealing with at most four different types of anomalies. In this work, a general mathematical model is proposed to give better performance in real-life test cases and to cover more than four types of anomalies. We note that the models can be generalized to detect and to classify anomalies for general time series data which is not necessarily generated from solar panel. We compared several techniques to detect and to classify anomalies including the auto-regressive integrated moving average model (ARIMA), neural networks, support vector machines and k-nearest-neighbors classification. We found that anomaly classification using the k-nearest-neighbors classification was able to accurately detect and classify 97% of the anomalies in our test set. The devices and algorithms have been tested with two small 12-volt solar panels.

In this report six different models for predicting the electrical spot price on the Nordic power exchange, Nord Pool, are developed and compared. They are evaluated against the already existing model as well as the naive test, which is a forecast where the last week’s observations are used as a prognosis for the coming week. The models developed are constructed so that the models for different time resolutions are combined to create a full model. Harmonic regression with a linear trend are used to identify a yearly trend while SARIMAX/SARIMA time series models are used on a daily and hourly basis to reveal dependencies in the data.   The model with the best prediction performance is shown to be a SARIMAX model with temperature as exogenous variable on a daily resolution, together with a SARIMA model on an hourly resolution. With an average MAPE of 12.69% and a MAPE2 of 6.90% it has the smallest prediction error out of all of the competing models when doing one week forecasts on the whole year 2009.

<p>Today, large-scale scientific applications are both data driven and distributed. To support the scale and inherent distribution of these applications, significant heterogeneous and geographically distributed resources are required over long periods of time to ensure adequate performance. Furthermore, the behavior of these applications depends on a large number of factors related to the application, the system software, the underlying hardware, and other running applications, as well as potential interactions among these factors.</p> <p>Most Grid application users are primarily concerned with obtaining the result of the application as fast as possible, without worrying about the details involved in monitoring and understanding factors affecting application performance. In this work, we aim to provide the application users with a simple and intuitive performance evaluation mechanism during the execution time of their long-running Grid applications or workflows. Our performance evaluation mechanism provides a qualitative and periodic assessment of the application's behavior by informing the user whether the application's performance is expected or unexpected. Furthermore, it can help improve overall application performance by informing and guiding fault-tolerance services when the application exhibits persistent unexpected performance behaviors.</p> <p>This thesis addresses the hypotheses that in order to qualitatively assess application behavioral states in long-running scientific Grid applications: (1) it is necessary to extract temporal information in performance time series data, and that (2) it is sufficient to extract variance and pattern as specific examples of temporal information. Evidence supporting these hypotheses can lead to the ability to qualitatively assess the overall behavior of the application and, if needed, to offer a most likely diagnostic of the underlying problem.</p> <p>To test the stated hypotheses, we develop and evaluate a general <em> qualitative performance analysis</em> framework that incorporates (a) techniques from time series analysis and machine learning to extract and learn from data, structural and temporal features associated with application performance in order to reach a qualitative interpretation of the application's behavior, and (b) mechanisms and policies to reason over time and across the distributed resource space about the behavior of the application. </p> <p>Experiments with two scientific applications from meteorology and astronomy comparing signatures generated from instantaneous values of performance data versus those generated from temporal characteristics support the former hypothesis that temporal information is necessary to extract from performance time series data to be able to accurately interpret the behavior of these applications. Furthermore, temporal signatures incorporating variance and pattern information generated for these applications reveal signatures that have distinct characteristics during well-performing versus poor-performing executions. This leads to the framework's accurate classification of instances of similar behaviors, which represents supporting evidence for the latter hypothesis. The proposed framework's ability to generate a qualitative assessment of performance behavior for scientific applications using temporal information present in performance time series data represents a step towards simplifying and improving the quality of service for Grid applications.</p> / Dissertation

Environmental flows are important to maintain the ecological integrity of the estuary. In a watershed, it is influenced by land use land cover (LULC) change, climate variability, and water regulations. San Antonio, Texas, the 8th largest city in the US, is likely to affect environmental flows to the San Antonio Bay/Guadalupe Estuary, due to rapid urbanization. Time series analysis was conducted at several stream gauging stations to assess trends in hydrologic variables. A bootstrapping method was employed to estimate the critical value for global significance. Results suggested a greater number of trends are observed than are expected to occur by chance. Stream gauging stations present in lower half of the watershed experienced increasing trend, whereas upper half experienced decreasing trends. A similar spatial pattern was not observed for rainfall. Winter season observed maximum number of trends. Wavelet analysis on hydrologic variables, suggested presence of multi-scale temporal variability; dominant frequencies in 10 to 15 year scale was observed in some of the hydrologic variables, with a decadal cycle. Dominant frequencies were also observed in 17 to 23 year scale with repeatability in 20 to 30 years. It is therefore important to understand various ecological processes that are dominant in this scale and quantify possible linkages among them. Genetic algorithm (GA) was used for calibration of the Hydrologic Simulation Program in FORTRAN (HSPF) model. Although, GA is computationally demanding, it is better than manual calibration. Parameter values obtained for the calibrated model had physical representation and were well within the ranges suggested in the literature. Information from LANDSAT images for the years 1987, 1999, and 2003 were introduced to HSPF to quantify the impact of LULC change on environmental flows. Modeling studies indicated, with increase in impervious surface, peak flows increased over the years. Wavelet analysis pointed, that urbanization also impacted storage. Modeling studies quantified, on average about 50% of variability in freshwater inflows could be attributed to variation in precipitation, and approximately 10% of variation in freshwater inflows could be attributed to LULC change. This study will help ecologist, engineers, scientist, and politicians in policy making pertinent to water resources management.

The telecommunications industry has been in the process of merging. However, the decline of Average Revenue Per User (ARPU) shows the saturation of mobile phone market. At the same time, the Directorate General of Telecommunications (DGT) has announced the implementation of number portability will be in January 2005. Therefore, the resort to sell handsets in high discounts is unavoidable to retain the existing customers and get the new subscribers. Because the high cost, short product lifecycle, diversity of usage, uncertainty of leading time in procurement and indefinite of consumers¡¦ demands, the management of handset inventory has been perplexed the telecommunication industry. This research tries to create an inventory model to provide the mobile phone companies a direction for deciding what the right order quantity is and when the re-order point is. It also can improve the satisfaction of customers and enforce the partnership with vendors. The vendor managed inventory (VMI) which is different from traditional inventory management is taken into consideration, and the target is the high-end function handset. The model will employ the data of average demand in the past few months and the decline curve of market price as the base. The relation of handsets has been created by using the characteristics of classified product. The methodology of time series analysis is used for this research. The construct of this handset inventory model is from the viewpoint of telecommunication industry.

Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.

Drought, which is an important result of the climate change, is one of the most serious natural hazards globally. It has been agreed all over the world that it has adverse impacts on the production of agriculture, which plays a major role in the economy of a country. Studies showed that the results of the drought directly affected the crop yields, and it seems that this negative impact will continue drastically soon. Moreover, many researches revealed that, Turkey will be affected from the results of climate change in many aspects, especially the agricultural production will encounter dry seasons after the rapid changes in the precipitation amount. Insurance is a well-established method, which is used to share the risk based on natural disasters by people and organizations. Furthermore, a new way of insuring against the weather shocks is designing index-based insurance, and it has gained special attention in many developing countries. In this study, our aim is to model weather index based drought insurance product to help the small holder farmers in the Cental Anatolia Region under different models. At first, time series techniques were applied to forecast the wheat yield relying on the past data. Then, the AMS (AgroMetShell) software outputs, NDVI (Normalized Difference Vegetation Index) values were used, and SPI values for distinct time steps were chosen to develop a basic threshold based drought insurance for each province. Linear regression equations were used to calculate the trigger points for weather index, afterwards based on these trigger levels / pure premium and indemnity calculations were made for each province separately. In addition to this, Panel Data Analysis were used to construct an alternative linear model for drought insurance. It can be helpful to understand the direct and actual effects of selected weather index measures on wheat yield and also reduce the basis risks for constructed contracts. A simple ratio was generated to compare the basis risk of the different index-based insurance contracts.

The variation of the composition of assemblages and the relative abundance of commercial prawns collected from the coastal waters of Yunlin was studied. The data of shrimp fishery was collected from Jan 1997 to Dec 2008 through the program of Integrated Assessment and Management of Environmental Resources for the Industrial District on the Outer-Bank of Yunlin Islands. The dominant species landed in this area are Parapenaeopsis hardwickii, Metapenaeus ensis, and Penaeus penicillatus. Their peak catch seasons are November to May, March to August, and July to March, respectively. The composition of the assemblages of commercial shrimps belonged to three groups according to the seasons: winter (December to February), spring to summer (March to July), and late summer to autumn (August to November). According to time series analysis, the relative abundance is auto-regressed to that of the previous month and the same month one year age. In addition, Jhuo-Shuei river runoff ware correlated to a significant decrease of the shrimp abundance five months later. Based on this information, we obtained a seasonal and factored ARIMA time series equation of the relative abundance of commercial shrimps.

<p>Unforeseen changes in production or consumption in power systems lead to changes in grid frequency. This can cause damages to the system, or to frequency sensitive equipment at the consumers. The system operator (SO) is the responsible for balancing production and consumption in the system. The regulating market is the market place where the SO can sell or purchase electricity in order to balance unforeseen events. Producers acting on the regulating market must be able to change their production levels fast (within minutes) when required. Hydropower is therefore suitable for trading on the regulating market because of its flexibility in power production. This thesis describes models that hydropower owners can use to generate optimal bidding strategies when the regulating market is considered.</p><p>When planning for trading on the market, the prices are not known. Therefore, the prices are considered as stochastic variables. The planning problems in this thesis are based on multi-stage stochastic optimization, where the uncertain power prices are represented by scenario trees. The scenario trees are generated by simulation of price scenarios, which is achieved by using a model based on ARIMA and Markov processes. Two optimization models are presented in this thesis:</p><p>* Model for generation of optimal bidding strategies for the regulating market.</p><p>* Model for generation of optimal bidding strategies for the spot market when trading on the regulating market is considered.</p><p>The described models are applied in a case study with real data from the Nordic power system.</p><p>Conclusions of the thesis are that the proposed approaches of modelling prices and generation of bidding strategies are possible to use, and that the models produces reasonable data when applied to real data.</p> / <p>Oväntade produktions- eller konsumtionsändringar i kraftsystem leder till ändringar i nätfrekvens. Detta kan orsaka skador på systemet eller på frekvenskänslig utrustning hos konsumenterna. Systemoperatören (SO) är den ansvarige för att balansera produktion och konsumtion i kraftsystemet. Till sin hjälp har SO reglermarknaden, som är den handelsplats där SO köper eller säljer el för att balansera oväntade händelser i systemet. Producenter som agerar på reglermarknaden måste snabbt (inom minuter) kunna ändra sina produktionsnivåer om så behövs. Vattenkraft är därför lämplig för handel på reglermarknaden på grund av dess flexibilitet i kraftproduktion. Denna avhandling beskriver modeller som vattenkraftägare kan använda för generering av optimala budstrategier då reglermarknaden beaktas.</p><p>När en producents planering för handel på marknaden utförs är marknadspriserna okända. Dessa är därför betraktade som stokastiska variabler. Planeringmodellerna som presenteras i denna avhandling är baserade på multi-periodisk stokastisk programmering, där de osäkra marknadspriserna är representerade av ett scenarieträd. Scenarierna i trädet genereras genom simulering av marknadspriser. En prismodell, baserad på ARIMA- och Markovprocesser, har därför utvecklats. Två olika optimeringsmodeller presenteras i denna avhandling:</p><p>* Model för generering av optimala budstrategier för reglermarknaden.</p><p>* Model för generering av optimala budstrategier för spotmarknaden då handel på reglermarknaden beaktas.</p><p>Modellerna tillämpas i en studie där data från den nordiska elmarknaden appliceras. Slutsatser i avhandlingen är att de föreslagna ansatserna för modellering av priser och generering av budstrategier är möjliga att anvÄanda, samt att modellerna producerar rimliga resultat när applicerade på verkliga data.</p>

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 364-373). Also available on the Internet.