Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv

Electric Water Heater Modeling, DR Approaches Analysis and Study of Consumer Comfort for Demand Response

With the smart energy management system household residential appliances is able to participate in the demand response events. To reduce peak load demand and complexities in the local infrastructure DR can play an important role now a days. This paper presents a study and analysis of several papers on residential EWH DR modeling and implementation. It shows an overview of analysis of the most used and recent DR models for EWH. It also shows the analysis of the used methods to model this and the used approach in several papers. Additionally, the discussed consumer comforts and obtainable benefits in several papers by participating in DR events is also shown here. The study and analysis in this paper will contribute to the future research and encourage the end users to participate in households DR events.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); SIMOCE (ANI|P2020 17690); and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Development of Economic Water Usage Sensor and Cyber-Physical Systems Co-Simulation Platform for Home Energy Saving

In this thesis, two Cyber-Physical Systems (CPS) approaches were considered to reduce residential building energy consumption. First, a flow sensor was developed for residential gas and electric storage water heaters. The sensor utilizes unique temperature changes of tank inlet and outlet pipes upon water draw to provide occupant hot water usage. Post processing of measured pipe temperature data was able to detect water draw events. Conservation of energy was applied to heater pipes to determine relative internal water flow rate based on transient temperature measurements. Correlations between calculated flow and actual flow were significant at a 95% confidence level. Using this methodology, a CPS water heater controller can activate existing residential storage water heaters according to occupant hot water demand. The second CPS approach integrated an open-source building simulation tool, EnergyPlus, into a CPS simulation platform developed by the National Institute of Standards and Technology (NIST). The NIST platform utilizes the High Level Architecture (HLA) co-simulation protocol for logical timing control and data communication. By modifying existing EnergyPlus co-simulation capabilities, NIST’s open-source platform was able to execute an uninterrupted simulation between a residential house in EnergyPlus and an externally connected thermostat controller. The developed EnergyPlus wrapper for HLA co-simulation can allow active replacement of traditional real-time data collection for building CPS development. As such, occupant sensors and simple home CPS product can allow greater residential participation in energy saving practices, saving up to 33% on home energy consumption nationally

Demand response model development for smart households using time of use tariffs and optimal control - the Isle of Wight energy autonomous community case study

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households

OPTIMAL ENERGY MANAGEMENT OF A HYBRID SOLAR WATER HEATING SYSTEM WITH GRID CONNECTION UNDER TIME-BASED PRICING

Published ThesisIn South Africa, 40 to 60% of the total energy of a normal residential building can be allocated to the heating of water. Traditionally, a standard electric storage tank-water heater (ESTWH) has been the main device for residential water heating within the country. However, as a result of the increase in the South African population, economy and living standards have led to an energy shortage, which has resulted in a steadily increasing electricity price. As an attempt to solve this electricity crisis, Eskom, the main electricity supplier, has recently introduced energy management activities such as energy efficiency (EE) and the use of renewable energy (RE) systems. On the one hand, the EE activities consist of reducing the total (overall) energy consumption during all the time periods, while load management (LM) activities aim to reduce the energy consumption during given time periods, such as peak times, when the Eskom grid cannot meet the demand. During peak times, the electricity consumption is charged at higher rates to encourage customers to shift their loads to off-peak and standard periods when the electricity is at a lower cost. This type of tariff is referred to as time-of-use (TOU) electricity tariff. With TOU, customers can therefore reduce their electricity bills by shifting load demands away from the peak time periods. On the other hand, in order to reduce the larger amount of residential peak load demand, renewable energy systems, such as the solar water heater (SWH), was recently introduced and implemented in South Africa as a replacement to the ESTWH. However, it has been observed that SWHs was not continuously meeting the thermal comfort of the users, under certain weather conditions. During winter, for instance, the amount of thermal energy required is greater than that of summer due to the temperature difference of the water that needs to be heated, while the solar radiation in winter is considerably less due to shorter days and the position of the sun with reference to the earth’s location. As a solution to this, the coupling of the SWH with the ESTWH, referred to as hybrid solar water heating (HSWH) system, is nowadays seen as technical and economic feasible option for water heating in South Africa. The system is composed of a solar collector that uses solar radiation to increase the temperature of water and the ESTWH, which stores the hot water. In the case of poor solar radiation, the SWH fails to increase the temperature of water to the comfortable level; therefore, the required temperature is maintained by the ESTWH. However, implementing optimal energy management of the HSWH can help to meet the required thermal comfort level while reducing the electricity cost, even more so when the TOU tariff is implemented. With this in mind, the aim of this work is to develop an optimal energy management model that will improve the operation efficiency of the HSWH. The main objective is to minimize the water heating energy cost from the grid by taking advantage of the TOU electricity tariff, meanwhile maximizing the thermal comfort level of hot water users. Simulations are performed using Matlab software, and the results demonstrate that operating the proposed hybrid system under the developed optimal energy management model reduced the operation cost when compared to a traditional ESTWH. In addition, the comparisons made in lifecycle costs of these systems shows that in the long run, the hybrid system will be the less costly option with a 49 % saving over a project lifetime of 20 years

Investigation of Electric Water Heaters as Demand Response Resources and Their Impact on Power System Operational Reliability

The electricity consumption has increased dramatically in past decades due to the improvement of people’s life standard and the increase of their incomes. Some uncertainties have occurred because of an increasing electricity consumption at the household level. As a result, the high power consumption of massive households will affect power system reliability. Recently, the traditional power grid is being transformed to the smart grid, which is an effective way to deal with these issues. The electricity utility could manage the demand side resources using different kinds of Demand Response (DR) methods. Residential resource is an important part besides industrial resource and commercial resource. With the deployment of Home Energy Management System (HEMS) and smart household devices, users’ behavior could be adjusted to respond to the utility signal. Electric Water Heaters (EWHs) account for a huge percentage of energy consumption among all the home appliances. Aggregated EWHs are idea candidates as demand response resources whose power consumption pattern can be modified because they not only consume lots of energy but also have heat storage capability. Therefore, EWHs can react to the optimal operation signal without affecting customers’ daily needs. In this way, electricity utility could treat EWHs as a kind of interruptible load to provide operating reserves to improve power system reliability. In this thesis, a Binary Particle Swarm Optimization (BPSO) algorithm is utilized to perform the optimization of EWHs. The goal of each EWH optimization using BPSO is to minimize the customers’ electricity cost. Therefore, Time-Of-Use (TOU) electricity rate is utilized as the DR incentive. Meanwhile, the customers’ daily need for hot water should be guaranteed, so a comfort level index is enforced in the optimization process. The thermal model of EWH and water usage profile are used to calculate the real-time hot water temperature. Aggregating thousands of EWHs will have positive influences on power system reliability when massive EWHs are utilized as interruptible loads. EWHs could compensate for the Unit Commitment Risk (UCR) considering the operating reserve capacity they can provide. The UCR reduction is used to calculate and analyze the influence of aggregated EWHs. A Reliability Test System is modified to test the capacity of aggregated EWHs in this study. Based on the simulation results, the proposed optimization strategy for EWHs is proved to be practical. The customers’ electricity bill has declined effectively and the user’s comfort level, considering different water temperature set point ranges, is ensured. This thesis provides a practicable scheme for residential customers to arrange their EWHs more reasonably. The simulation results show the aggregated EWHs’ load curve and indicate that the proposed method shifts aggregated EWHs load effectively during some peak hours. According to the calculation results of UCR reduction, the aggregated EWHs is turned out to be a great candidate for power system to improve the reliability during peak-hours