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Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems
Thesis (MEng)--Stellenbosch University, 2016.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2016
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| _version_ | 1867614135983800320 |
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| access_status_str | Open Access |
| author | Blignault, George William |
| author2 | Vermeulen, H. J. |
| author_browse | Blignault, George William Vermeulen, H. J. |
| author_facet | Vermeulen, H. J. Blignault, George William |
| author_sort | Blignault, George William |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2016. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/100184 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:47:14.419Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/100184 Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems Blignault, George William Vermeulen, H. J. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Hot-water heating Energy consumption -- management Energy consumption -- Social conditions Energy consumption -- Economic conditions UCTD Thesis (MEng)--Stellenbosch University, 2016. ENGLISH ABSTRACT: The introduction of Energy Management (EM) interventions aimed at reducing electrical energy consumption in the residential, commercial and industrial load sectors has expanded rapidly on a global scale in recent years. These programs are driven by environmental concerns, capacity constraints experienced in generation and transmission and the need to improve end-use efficiency. The implementation of EM schemes often involves financial incentives funded by governments and utilities. Measurement and Verification (M&V) performance assessments aimed at determining the savings impacts form an integral part of the management of these EM incentive programmes. M&V baseline development involves the development and implementation of accurate models that relate the energy consumption of a targeted load to variable energy-governing factors in order to determine actual savings impacts. The electrical energy consumption associated with sanitary water heating represents a sizeable component of the cumulative energy consumption associated with a number of load categories found in the various load sectors. In general, the electricity consumption profiles associated with sanitary hot water consumption correlates closely with the household electricity consumption profiles found in the residential load sector, particularly in the sense that it is influenced by the same socio-economic factors and human behavioural patterns. Soft computing methods have been employed successfully for residential load prediction, as these are tolerant of stochastic behaviour and uncertainty and do not require exact input to output matching. Particular success in the field of residential Short Term Load Forecasting (STLF) has been achieved using Adaptive Neuro-Fuzzy Inference Systems (ANFIS). An ANFIS load forecasting model with a long prediction horizon of up to a year is found to be capable of reasonable modelling accuracy for the estimation of the time-series profile of a system. It also exhibits very good prediction accuracy when calculating the total energy use over time of that profile. The load data used in this study is of student residence heat pump power consumption profiles and spans over four years with 48 samples for each day. The training inputs that are considered other than the load are the time of the day, the day of the week, the day of the year and the temperature. After the proof of concept, a comparative case study is performed with the view to explore optimal configurations of differing inputs to the ANFIS method. The effects of compartmentalising the dataset into subsets representing different characteristics, thereby deriving different models representing different cyclic periods, are also explored. It is found that compartmentalising the load model into 48 ANFIS sub-models, each serving a specific half-hourly time period in the day, results in the most best modelling accuracy. AFRIKAANSE OPSOMMING: Die bekendstelling van Energie Bestuur (EB) ingrypings wat gerig is op die vermindering van elektriese energieverbruik in die residensiële, kommersiële en industriële lassektore het vinnig uitgebrei op 'n globale skaal in onlangse jare. Hierdie programme word gedryf deur omgewinskwessies, ervaring van kapasiteitsbeperkings tydens die opwekking en transmissie van krag en die behoefte om einde gebruik doeltreffendheid te verbeter. Die implementering van EB skemas behels dikwels finansiële aansporings wat deur regerings en kragopwekkende instansies befonds word. Meting en verifikasie (M&V) prestasiebeoordelings wat gemik is op die bepaling van die besparings impak vorm 'n integrale deel van die bestuur van hierdie EB aansporings programme. M&V basislyn ontwikkeling behels die ontwikkeling en implimentering van akkurate modelle wat die energieverbruik van n teikenlas aan energie veranderlike faktore bind ten einde die besparingsimakte te bepaal. Die elektriese energieverbruik wat verband hou met sanitêre water verwarming verteenwoordig 'n groot komponent van die kumulatiewe energieverbruik wat verband hou met 'n aantal las kategorieë in die verskeie lassektore. Die elektrisiteitsverbruik profiele wat verband hou met sanitêre warm waterverbruik korreleer oor die algemeen met die huishoudelike elektrisiteitsverbruik profiele van die residensiële sektor, veral in die sin dat dit beïnvloed word deur dieselfde sosio-ekonomiese faktore en menslike gedragspatrone. Sagte rekenaar metodes is al suksesvol gemplimenteer vir residensiële lasvoorspelling, aangesien dit verdraagsaam van stogastiese gedrag en onsekerheid is en nie dat die insette en uitsette presies ooreen stem nie. Sukses is veral in die gebied van residensiële Kort Termyn lasvooruitskatting (KTLV) behaal met behulp van Adaptive Neuro-Fuzzy Inferensie Systems (ANFIS). Dit is gevind dat 'n ANFIS las voorspellings model met 'n lang voorspelling horison van tot 'n jaar in staat is tot redelike modellering akkuraatheid vir die skatting van die tyd-reeks profiel van 'n stelsel. Dit vertoon ook baie goeie voorspelling akkuraatheid by die berekening van die totale energieverbruik van die profiel oor tyd. Die las data wat in hierdie studie gebruik word is van studentekoshuise se warmtepomp kragverbruik profiele en strek oor vier jaar met 48 monsters vir elke dag. Die opleiding insette wat beskou word, anders as die las, is die tyd van die dag, die dag van die week, die dag van die jaar en die temperatuur. Na afloop van die bewys van die konsep is 'n vergelykende gevallestudie uitgevoer met die doel om optimale konfigurasies van verskillende insette tot die ANFIS metode te verken. Die gevolge van kompartimentalisering van die datastel in sub-versamelings wat verskillende eienskappe verteenwoordig, en dus verskillende modelle verteenwoordigend van verskillende sikliese periodes aflei, word ook ondersoek. Daar word bevind dat kompartimentalisering van die lasmodel in 48 ANFIS sub-modelle, wat elk 'n spesifieke half-uurlikse tydperk in die dag bedien, lei tot die beste modelleringsakkuraatheid. 2016-12-22T13:24:34Z 2016-12-22T13:24:34Z 2016-12 Thesis http://hdl.handle.net/10019.1/100184 en_ZA Stellenbosch University xxvii, 167 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Hot-water heating Energy consumption -- management Energy consumption -- Social conditions Energy consumption -- Economic conditions UCTD Blignault, George William Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title | Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title_full | Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title_fullStr | Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title_full_unstemmed | Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title_short | Modelling of sanitary hot water energy consumption using adaptive neuro-fuzzy inference systems |
| title_sort | modelling of sanitary hot water energy consumption using adaptive neuro fuzzy inference systems |
| topic | Hot-water heating Energy consumption -- management Energy consumption -- Social conditions Energy consumption -- Economic conditions UCTD |
| url | http://hdl.handle.net/10019.1/100184 |
| work_keys_str_mv | AT blignaultgeorgewilliam modellingofsanitaryhotwaterenergyconsumptionusingadaptiveneurofuzzyinferencesystems |