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Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller.
Daniel de Bruyn; Ben Kotze; William Hurst. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures 2021, 6, 67 .
AMA StyleDaniel de Bruyn, Ben Kotze, William Hurst. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures. 2021; 6 (5):67.
Chicago/Turabian StyleDaniel de Bruyn; Ben Kotze; William Hurst. 2021. "A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating." Infrastructures 6, no. 5: 67.
Carbon emission is a prominent issue, and smart urban solutions have the technological capabilities to implement change. The technologies for creating smart energy systems already exist, some of which are currently under wide deployment globally. By investing in energy efficiency solutions (such as the smart meter), research shows that the end-user is able to not only save money, but also reduce their household’s carbon footprint. Therefore, in this paper, the focus is on the end-user, and adopting a quantitative analysis of the perception of 1365 homes concerning the smart gas meter installation. The focus is on linking end-user attributes (age, education, social class and employment status) with their opinion on reducing energy, saving money, changing home behaviour and lowering carbon emissions. The results show that there is a statistical significance between certain attributes of end-users and their consideration of smart meters for making beneficial changes. In particular, the investigation demonstrates that the employment status, age and social class of the homeowner have statistical significance on the end-users’ variance; particularly when interested in reducing their bill and changing their behaviour around the home.
William Hurst; Bedir Tekinerdogan; Ben Kotze. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities 2020, 3, 1173 -1186.
AMA StyleWilliam Hurst, Bedir Tekinerdogan, Ben Kotze. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities. 2020; 3 (4):1173-1186.
Chicago/Turabian StyleWilliam Hurst; Bedir Tekinerdogan; Ben Kotze. 2020. "Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint." Smart Cities 3, no. 4: 1173-1186.
Automatic Guided Vehicles (AGVs) are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.
Ben Kotze; Gerrit Jordaan. Investigation of Matlab® as Platform in Navigation and Control of an Automatic Guided Vehicle Utilising an Omnivision Sensor. Sensors 2014, 14, 15669 -15686.
AMA StyleBen Kotze, Gerrit Jordaan. Investigation of Matlab® as Platform in Navigation and Control of an Automatic Guided Vehicle Utilising an Omnivision Sensor. Sensors. 2014; 14 (9):15669-15686.
Chicago/Turabian StyleBen Kotze; Gerrit Jordaan. 2014. "Investigation of Matlab® as Platform in Navigation and Control of an Automatic Guided Vehicle Utilising an Omnivision Sensor." Sensors 14, no. 9: 15669-15686.