Elbesparing med pelletkaminer och solvärme i direktelvärmda småhus

The aim of this study was to investigate how electricallyheated houses can be converted to using wood pellet and solarheating. There are a large number of wood pellet stoves on themarket. Many stoves have a water jacket, which gives anopportunity to distribute the heat to domestic hot water and aradiator heating system. Three typical Swedish houses with electric resistanceheating have been studied. Fourteen different system conceptsusing wood pellet stoves and solar heating systems have beenevaluated. The systems and the houses have been simulated indetail using TRNSYS. The houses have been divided in up to 10different zones and heat transfer by air circulation throughdoorways and open doors have been simulated. The pellet stoveswere simulated using a recently developed TRNSYS component,which models the start- and stop phases, emissions and thedynamic behaviour of the stoves. The model also calculates theCO-emissions. Simulations were made with one stove without awater jacket and two stoves with different fractions of thegenerated heat distributed in the water circuit. Simulations show that the electricity savings using a pelletstove are greatly affected by the house plan, the systemchoice, if the internal doors are open or closed and thedesired level of comfort. Installing a stove with awater-jacket connected to a radiator system and a hot waterstorage has the advantage that heat can be transferred todomestic hot water and be distributed to other rooms. Suchsystems lead to greater electricity savings, especially inhouses having a traditional layout. It was found that not allrooms needed radiators and that it was more effective in mostcases t use a stove with a higher fraction of the heatdistributed by the water circuit. The economic investigation shows that installing a woodpellet stove without a water jacket gives the lowest totalenergy- and capital costs in the house with an open plan (fortoday's energy prices and the simulated comfort criteria). Inthe houses with a traditional layout a pellet stove givesslightly higher costs than the reference house having onlyelectrical resistance heating due to the fact that less heatingcan be replaced. The concepts including stoves with a waterjacket all give higher costs than the reference system, but theconcept closest to be economical is a system with a bufferstore, a stove with a high fraction of the heat distributed bythe water circuit, a new water radiator heating system and asolar collector. Losses from stoves can be divided into: flue gas lossesincluding leakage air flow when the stove is not in operation;losses during start and stop phases; and losses due to a highair factor. An increased efficiency of the stoves is importantboth from a private economical point of view, but also from theperspective that there can be a lack of bio fuel in the nearfuture also in Sweden. From this point of view it is alsoimportant to utilize as much solar heat as possible. Theutilization of solar heat is low in the simulated systems,depending on the lack of space for a large buffer store. The simulations have shown that the annual efficiency ismuch lower that the nominal efficiency at full power. Thesimulations have also shown that changing the control principlefor the stove can improve efficiency and reduce theCO-emissions. Today's most common control principle for stovesis the on/off control, which results in many starts and stopsand thereby high CO-emissions. A more advanced control varyingthe heating rate from maximum to minimum to keep a constantroom temperature reduces the number of starts and stops andthereby the emissions. Also the efficiency can be higher withsuch a control, and the room temperature will be kept at a moreconstant temperature providing a higher comfort.

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.