A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Short-term forecasting of power demand in the Nord Pool market

Sähkönkulutuksen lyhyen aikavälin ennustamista on tutkittu jo pitkään. Pohjoismaisien sähkömarkkinoiden vapautuminen on vaikuttanut sähkönkulutuksen ennustamiseen. Aluksi työssä perehdyttiin aiheeseen liittyvään kirjallisuuteen. Sähkönkulutuksen käyttäytymistä tutkittiin eri aikoina. Lämpötila tilastojen käyttökelpoisuutta arvioitiin sähkönkulutusennustetta ajatellen. Kulutus ennusteet tehtiin tunneittain ja ennustejaksona käytettiin yhtä viikkoa. Työssä tutkittiin sähkönkulutuksen- ja lämpötiladatan saatavuutta ja laatua Nord Poolin markkina-alueelta. Syötettävien tietojen ominaisuudet vaikuttavat tunnittaiseen sähkönkulutuksen ennustamiseen. Sähkönkulutuksen ennustamista varten mallinnettiin kaksi lähestymistapaa. Testattavina malleina käytettiin regressiomallia ja autoregressiivistä mallia (autoregressive model, ARX). Mallien parametrit estimoitiin pienimmän neliösumman menetelmällä. Tulokset osoittavat että kulutus- ja lämpötiladata on tarkastettava jälkikäteen koska reaaliaikaisen syötetietojen laatu on huonoa. Lämpötila vaikuttaa kulutukseen talvella, mutta se voidaan jättää huomiotta kesäkaudella. Regressiomalli on vakaampi kuin ARX malli. Regressiomallin virhetermi voidaan mallintaa aikasarjamallia hyväksikäyttäen.

Modelling of changes in electricity end-use and their impacts on electricity distribution

The electricity distribution sector will face significant changes in the future. Increasing reliability demands will call for major network investments. At the same time, electricity end-use is undergoing profound changes. The changes include future energy technologies and other advances in the field. New technologies such as microgeneration and electric vehicles will have different kinds of impacts on electricity distribution network loads. In addition, smart metering provides more accurate electricity consumption data and opportunities to develop sophisticated load modelling and forecasting approaches. Thus, there are both demands and opportunities to develop a new type of long-term forecasting methodology for electricity distribution. The work concentrates on the technical and economic perspectives of electricity distribution. The doctoral dissertation proposes a methodology to forecast electricity consumption in the distribution networks. The forecasting process consists of a spatial analysis, clustering, end-use modelling, scenarios and simulation methods, and the load forecasts are based on the application of automatic meter reading (AMR) data. The developed long-term forecasting process produces power-based load forecasts. By applying these results, it is possible to forecast the impacts of changes on electrical energy in the network, and further, on the distribution system operator’s revenue. These results are applicable to distribution network and business planning. This doctoral dissertation includes a case study, which tests the forecasting process in practice. For the case study, the most prominent future energy technologies are chosen, and their impacts on the electrical energy and power on the network are analysed. The most relevant topics related to changes in the operating environment, namely energy efficiency, microgeneration, electric vehicles, energy storages and demand response, are discussed in more detail. The study shows that changes in electricity end-use may have radical impacts both on electrical energy and power in the distribution networks and on the distribution revenue. These changes will probably pose challenges for distribution system operators. The study suggests solutions for the distribution system operators on how they can prepare for the changing conditions. It is concluded that a new type of load forecasting methodology is needed, because the previous methods are no longer able to produce adequate forecasts.

Alueellinen sähkökuormien kehitysennuste Rovaniemen Verkko Oy:n jakelualueella

Tämän työn tavoitteena on skenaarioiden avulla luoda pitkän aikavälin alueellinen sähkökuormien kehitysennuste Rovaniemen Verkko Oy:lle. Pitkän aikavälin kuormitusennusteet ovat välttämättömiä verkon kehittämisen pohjalle, jotta verkko voidaan mitoittaa vastaamaan kuormitusta pitkälle tulevaisuuteen tekniset ja taloudelliset vaatimukset huomioiden. Kuormitusennusteen onkin jatkossa tarkoitus toimia apuvälineenä verkon strategisessa kehittämisessä. Pohjana kuormitusennusteissa on tilastokeskuksen ja Rovaniemen kaupungin väestö- ja työpaikkaennusteet. Väestöennusteiden ja erilaisten rakentamistilastoiden avulla arvioidaan uudisrakentamisen määrä tulevaisuudessa. Uudisrakentamisen kuormitusvaikutuksiin päästään työssä määritettyjen paikallisten ja rakennustyyppikohtaisten sähkön ominaiskulutuksien avulla. Kuormituksien alueellinen sijoittautuminen arvioidaan kaavoituksen ja kaupungin maankäytön toteuttamisohjelman avulla. Työssä tutkitaan myös tulevaisuudessa sähkönkäytössä tapahtuvien useiden muutosten vaikutusta alueelliseen kuormitukseen. Näitä muutoksia ovat muun muassa sähköautojen, hajautetun tuotannon, lämpöpumppujen ja kysynnän jouston lisääntyminen. Myös rakennusten jatkuvasti parantuva energiatehokkuus aiheuttaa muutoksia sähkön kulutukseen.

Pientalojen ilmalämpöpumput sähköverkossa

Työssä tutkitaan ilmalämpöpumppujen kokonaisvaltaista vaikutusta sähköverkkoon. Tarkastelu aloitetaan lämpöpumppujen toiminnasta ja rakenteesta, josta jatketaan laitteen käytettävyyteen ja muiden lämmitysmenetelmien vertailuun. Sähköisten ominaisuuksien tarkastelussa pohditaan ilmalämpöpumppujen vaikutusta suomalaiseen sähköverkkoon muun muassa yleissähkötekniikan, taloudellisuuden ja energiatehokkuuden sekä häiriöiden kannalta. Tämä tutkielma rajoittuu pientaloihin, ja niihin asennettuihin ilma-ilmalämpöpumppuihin. Työn loppupäätelmänä on, että ilmalämpöpumppujen käytöstä ei juuri aiheudu vaikutuksia suomalaiseen sähköverkkoon. Suurimmat ilmalämpöpumppujen käytöstä syntyvät seuraukset kohdistuvat sähköverkkoyhtiöihin, joihin ilmalämpöpumput aiheuttavat taloudellisia menetyksiä. Suuret ja tulevaisuudessa kasvavat ilmalämpöpumppumäärät aiheuttavat sähköntuotantoon lisätehontarvetta huippukuorman aikaan. Toisaalta välitehoalueella tehontarve sekä energiankulutus pienenevät. Sähköverkoissa ei ole toistaiseksi havaittu ilmalämpöpumpuista johtuvia häiriöitä.

On the Analysis and Control of a Linear Synchronous Servomotor with a Flexible Load

Industry's growing need for higher productivity is placing new demands on mechanisms connected with electrical motors, because these can easily lead to vibration problems due to fast dynamics. Furthermore, the nonlinear effects caused by a motor frequently reduce servo stability, which diminishes the controller's ability to predict and maintain speed. Hence, the flexibility of a mechanism and its control has become an important area of research. The basic approach in control system engineering is to assume that the mechanism connected to a motor is rigid, so that vibrations in the tool mechanism, reel, gripper or any apparatus connected to the motor are not taken into account. This might reduce the ability of the machine system to carry out its assignment and shorten the lifetime of the equipment. Nonetheless, it is usually more important to know how the mechanism, or in other words the load on the motor, behaves. A nonlinear load control method for a permanent magnet linear synchronous motor is developed and implemented in the thesis. The purpose of the controller is to track a flexible load to the desired velocity reference as fast as possible and without awkward oscillations. The control method is based on an adaptive backstepping algorithm with its stability ensured by the Lyapunov stability theorem. As a reference controller for the backstepping method, a hybrid neural controller is introduced in which the linear motor itself is controlled by a conventional PI velocity controller and the vibration of the associated flexible mechanism is suppressed from an outer control loop using a compensation signal from a multilayer perceptron network. To avoid the local minimum problem entailed in neural networks, the initial weights are searched for offline by means of a differential evolution algorithm. The states of a mechanical system for controllers are estimated using the Kalman filter. The theoretical results obtained from the control design are validated with the lumped mass model for a mechanism. Generalization of the mechanism allows the methods derived here to be widely implemented in machine automation. The control algorithms are first designed in a specially introduced nonlinear simulation model and then implemented in the physical linear motor using a DSP (Digital Signal Processor) application. The measurements prove that both controllers are capable of suppressing vibration, but that the backstepping method is superior to others due to its accuracy of response and stability properties.

Design and Simulation of High-Speed Rotating Electrical Machinery

Global energy consumption has been increasing yearly and a big portion of it is used in rotating electrical machineries. It is clear that in these machines energy should be used efficiently. In this dissertation the aim is to improve the design process of high-speed electrical machines especially from the mechanical engineering perspective in order to achieve more reliable and efficient machines. The design process of high-speed machines is challenging due to high demands and several interactions between different engineering disciplines such as mechanical, electrical and energy engineering. A multidisciplinary design flow chart for a specific type of high-speed machine in which computer simulation is utilized is proposed. In addition to utilizing simulation parallel with the design process, two simulation studies are presented. The first is used to find the limits of two ball bearing models. The second is used to study the improvement of machine load capacity in a compressor application to exceed the limits of current machinery. The proposed flow chart and simulation studies show clearly that improvements in the high-speed machinery design process can be achieved. Engineers designing in high-speed machines can utilize the flow chart and simulation results as a guideline during the design phase to achieve more reliable and efficient machines that use energy efficiently in required different operation conditions.

Evaluation of PostgreSQL replication and load balancing implementations

In this final project the high availability options for PostgreSQL database management system were explored and evaluated. The primary objective of the project was to find a reliable replication system and implement it to a production environment. The secondary objective was to explore different load balancing methods and compare their performance. The potential replication methods were thoroughly examined, and the most promising was implemented to a database system gathering weather information in Lithuania. The different load balancing methods were tested performance wise with different load scenarios and the results were analysed. As a result for this project a functioning PostgreSQL database replication system was built to the Lithuanian Hydrometeorological Service's headquarters, and definite guidelines for future load balancing needs were produced. This study includes the actual implementation of a replication system to a demanding production environment, but only guidelines for building a load balancing system to the same production environment.

Relationship between electrical conductivity and water content in peat growth medium

Tiivistelmä: Turvekasvualustan sähkönjohtavuuden ja vesipitoisuuden riippuvuus mitattuna TDR-käsimittarilla

A Dynamic forecasting model for the Finnish pulp export price

Summary

Relationship of breast fillet deboning time to shear force, pH, cooking loss and color in broilers stunned by high electrical current

Selostus: Korkealla virranvoimakkuudella tainnutettujen broilereiden rintafileen irroitushetken vaikutus lihaksen leikkausvoiman vastukseen, pH:hon, keittohävikkiin ja väriin