Predictive model of electrical energy consumption in a South African food retail store
| dc.contributor.advisor | Moorlach, Mascha | |
| dc.contributor.advisor | Ngoepe, Malebogo | |
| dc.contributor.author | Pietrangeli, Sven | |
| dc.date.accessioned | 2026-08-24T09:49:18Z | |
| dc.date.available | 2026-08-24T09:49:18Z | |
| dc.date.issued | 2026 | |
| dc.date.updated | 2026-08-24T09:44:40Z | |
| dc.description.abstract | The supply of energy, especially electrical energy, is a key driver in supporting the development of any economy and the industries within those economies. Factors such as electricity prices significantly influence the cost of living, especially in relation to the food supply chain. Commercial food retail stores have been conservative in their uptake of energy efficiency (EE) initiatives. The escalating costs of electricity and the projected trends have mobilised some prominent food retailers to invest in EE initiatives and benefit from incentives, such as Section 12-L of the Income Tax Act, 1962. The typical food retail store has an electrical energy consumption predominantly linked to the refrigeration of foodstuffs, such as fresh produce, processed foods, or storage of products, in some cases +/- 50% of the total. This is followed closely by lighting +/-20% and Heating, Ventilation & Air-conditioning (HVAC) +/-15%; the balance is taken up by other miscellaneous users such as office space, cashier systems, security systems and other more general loads. This research aims to develop an electrical energy share model (EESM), which will give a framework for food retail stores to apply overall electrical consumption data to aid in deciding on interventions and estimate possible savings. The EESM provides a predictive method in terms of what energy-consuming categories (ECC) are active and to what degree of the total electrical consumption. The model then allows the application of Minimal Improvement Percentages (MIP) to determine the potential electrical saving per ECC. This allows for decisions to be taken on what ECC to apply EE initiatives to and by what electrical energy-saving technology. A usage coefficient accounts for behavioural patterns in terms of a percentage of a 24-hour day and can be adjusted as the operation times of the store vary. Collected data results were applied in a case study format to a larger pool of collected data of differently sized SPAR food retail stores, i.e., Kwik Spar, Spar and SuperSpar. The produced post EESM data allows for comparison, at a later stage, if stores decide to implement any EE initiatives. EE projects in the food retail sector will become more viable with increasing electrical prices, and estimations of these savings can be more accurately predicted by applying the electrical energy share model to overall or total electrical energy data of a store, especially in the case where specific or differentiated data is not available. | |
| dc.identifier.apacitation | Pietrangeli, S. (2026). <i>Predictive model of electrical energy consumption in a South African food retail store</i>. (). University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/43714 | en_ZA |
| dc.identifier.chicagocitation | Pietrangeli, Sven. <i>"Predictive model of electrical energy consumption in a South African food retail store."</i> ., University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2026. http://hdl.handle.net/11427/43714 | en_ZA |
| dc.identifier.citation | Pietrangeli, S. 2026. Predictive model of electrical energy consumption in a South African food retail store. . University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. http://hdl.handle.net/11427/43714 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Pietrangeli, Sven AB - The supply of energy, especially electrical energy, is a key driver in supporting the development of any economy and the industries within those economies. Factors such as electricity prices significantly influence the cost of living, especially in relation to the food supply chain. Commercial food retail stores have been conservative in their uptake of energy efficiency (EE) initiatives. The escalating costs of electricity and the projected trends have mobilised some prominent food retailers to invest in EE initiatives and benefit from incentives, such as Section 12-L of the Income Tax Act, 1962. The typical food retail store has an electrical energy consumption predominantly linked to the refrigeration of foodstuffs, such as fresh produce, processed foods, or storage of products, in some cases +/- 50% of the total. This is followed closely by lighting +/-20% and Heating, Ventilation & Air-conditioning (HVAC) +/-15%; the balance is taken up by other miscellaneous users such as office space, cashier systems, security systems and other more general loads. This research aims to develop an electrical energy share model (EESM), which will give a framework for food retail stores to apply overall electrical consumption data to aid in deciding on interventions and estimate possible savings. The EESM provides a predictive method in terms of what energy-consuming categories (ECC) are active and to what degree of the total electrical consumption. The model then allows the application of Minimal Improvement Percentages (MIP) to determine the potential electrical saving per ECC. This allows for decisions to be taken on what ECC to apply EE initiatives to and by what electrical energy-saving technology. A usage coefficient accounts for behavioural patterns in terms of a percentage of a 24-hour day and can be adjusted as the operation times of the store vary. Collected data results were applied in a case study format to a larger pool of collected data of differently sized SPAR food retail stores, i.e., Kwik Spar, Spar and SuperSpar. The produced post EESM data allows for comparison, at a later stage, if stores decide to implement any EE initiatives. EE projects in the food retail sector will become more viable with increasing electrical prices, and estimations of these savings can be more accurately predicted by applying the electrical energy share model to overall or total electrical energy data of a store, especially in the case where specific or differentiated data is not available. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - energy consumption KW - food retail LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Predictive model of electrical energy consumption in a South African food retail store TI - Predictive model of electrical energy consumption in a South African food retail store UR - http://hdl.handle.net/11427/43714 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/43714 | |
| dc.identifier.vancouvercitation | Pietrangeli S. Predictive model of electrical energy consumption in a South African food retail store. []. University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43714 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Mechanical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | energy consumption | |
| dc.subject | food retail | |
| dc.title | Predictive model of electrical energy consumption in a South African food retail store | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |