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Article

A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating

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iDigi-Tech, First National Bank, Gauteng, Randburg 2194, South Africa
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Department of Electrical, Electronic and Computer Engineering, Central University of Technology, Centre for Sustainable Smart Cities, Free State, Private Bag X20539, Bloemfontein 9300, South Africa
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Information Technology Group, Wageningen University and Research, Leeuwenborch, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: GM Shafiullah
Received: 4 March 2021 / Revised: 16 April 2021 / Accepted: 22 April 2021 / Published: 30 April 2021
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. View Full-Text
Keywords: solar geyser; profile usage; profile weather conditions; fuzzy logic; partial clustering; hidden Markov model; Esp8266 solar geyser; profile usage; profile weather conditions; fuzzy logic; partial clustering; hidden Markov model; Esp8266
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MDPI and ACS Style

de Bruyn, D.N.; Kotze, B.; Hurst, W. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures 2021, 6, 67. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6050067

AMA Style

de Bruyn DN, Kotze B, Hurst W. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures. 2021; 6(5):67. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6050067

Chicago/Turabian Style

de Bruyn, Daniel N., Ben Kotze, and William Hurst. 2021. "A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating" Infrastructures 6, no. 5: 67. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6050067

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