Combined solar and pellet heating systems for single-family houses : How to achieve decreased electricity usage, increased system efficiency and increased solar gains

In Sweden, there are about 0.5 million single-family houses that are heated by electricity alone, and rising electricity costs force the conversion to other heating sources such as heat pumps and wood pellet heating systems. Pellet heating systems for single-family houses are currently a strongly growing market. Future lack of wood fuels is possible even in Sweden, and combining wood pellet heating with solar heating will help to save the bio-fuel resources. The objectives of this thesis are to investigate how the electrically heated single-family houses can be converted to pellet and solar heating systems, and how the annual efficiency and solar gains can be increased in such systems. The possible reduction of CO-emissions by combining pellet heating with solar heating has also been investigated. Systems with pellet stoves (both with and without a water jacket), pellet boilers and solar heating have been simulated. Different system concepts have been compared in order to investigate the most promising solutions. Modifications in system design and control strategies have been carried out in order to increase the system efficiency and the solar gains. Possibilities for increasing the solar gains have been limited to investigation of DHW-units for hot water production and the use of hot water for heating of dishwashers and washing machines via a heat exchanger instead of electricity (heat-fed appliances). Computer models of pellet stoves, boilers, DHW-units and heat-fed appliances have been developed and the parameters for the models have been identified from measurements on real components. The conformity between the models and the measurements has been checked. The systems with wood pellet stoves have been simulated in three different multi-zone buildings, simulated in detail with heat distribution through door openings between the zones. For the other simulations, either a single-zone house model or a load file has been used. Simulations were carried out for Stockholm, Sweden, but for the simulations with heat-fed machines also for Miami, USA. The foremost result of this thesis is the increased understanding of the dynamic operation of combined pellet and solar heating systems for single-family houses. The results show that electricity savings and annual system efficiency is strongly affected by the system design and the control strategy. Large reductions in pellet consumption are possible by combining pellet boilers with solar heating (a reduction larger than the solar gains if the system is properly designed). In addition, large reductions in carbon monoxide emissions are possible. To achieve these reductions it is required that the hot water production and the connection of the radiator circuit is moved to a well insulated, solar heated buffer store so that the boiler can be turned off during the periods when the solar collectors cover the heating demand. The amount of electricity replaced using systems with pellet stoves is very dependant on the house plan, the system design, if internal doors are open or closed and the comfort requirements. Proper system design and control strategies are crucial to obtain high electricity savings and high comfort with pellet stove systems. The investigated technologies for increasing the solar gains (DHW-units and heat-fed appliances) significantly increase the solar gains, but for the heat-fed appliances the market introduction is difficult due to the limited financial savings and the need for a new heat distribution system. The applications closest to market introduction could be for communal laundries and for use in sunny climates where the dominating part of the heat can be covered by solar heating. The DHW-unit is economical but competes with the internal finned-tube heat exchanger which is the totally dominating technology for hot water preparation in solar combisystems for single-family houses.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Methodological issues in applying Location Models to Rural areas

Location Models are usedfor planning the location of multiple service centers in order to serve a geographicallydistributed population. A cornerstone of such models is the measure of distancebetween the service center and a set of demand points, viz, the location of thepopulation (customers, pupils, patients and so on). Theoretical as well asempirical evidence support the current practice of using the Euclidian distancein metropolitan areas. In this paper, we argue and provide empirical evidencethat such a measure is misleading once the Location Models are applied to ruralareas with heterogeneous transport networks. This paper stems from the problemof finding an optimal allocation of a pre-specified number of hospitals in alarge Swedish region with a low population density. We conclude that the Euclidianand the network distances based on a homogenous network (equal travel costs inthe whole network) give approximately the same optimums. However networkdistances calculated from a heterogeneous network (different travel costs indifferent parts of the network) give widely different optimums when the numberof hospitals increases.  In terms ofaccessibility we find that the recent closure of hospitals and the in-optimallocation of the remaining ones has increased the average travel distance by 75%for the population. Finally, aggregation the population misplaces the hospitalsby on average 10 km.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.