A Model for Predicting Unregulated Energy Usage

Frimpong, Emmanuel Asuming; Twumasi, Elvis

doi:10.1109/powerafrica49420.2020.9219844

Cited by 1 publication

(1 citation statement)

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“…This paper is an enhanced and updated version of conference paper (Frimpong and Twumasi, 2020). The prediction of unregulated energy has become critical since unregulated energy loads (UELs) can now take up to 50% of the total energy of energy efficient buildings (Birch et al , 2020; HQM, 2018).…”

Section: Introductionmentioning

confidence: 99%

Prediction of unregulated energy usage in office buildings

Frimpong

Twumasi

2021

IJBPA

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PurposeThe paper presents a technique for predicting the energy consumption of unregulated energy loads (UELs) in office buildings. It also presents an approach for determining a set of optimum values required by the technique.Design/methodology/approachThe proposed technique uses the optimum power drawn and optimum usage period in three modes of device operation, for the prediction. The usage modes are active mode, idle (low active) mode and off mode. The optimum powers and usage times are inserted into a linear mathematical equation to predict the energy consumption. Regarding the approach for determining the optimum values, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to a range of values obtained from field measurements. The proposed prediction method and approach for determining optimum values were tested using data of energy consumption of UELs in a case study facility.FindingsTest results show that the method for predicting the energy consumption of UELs in offices is highly accurate and suitable for adoption by energy modelers, building designers and building regulatory agencies. The approach for determining the optimum values is also effective and can aid the establishment of workable benchmark values.Originality/valueA new and simple model has been developed for the prediction of unregulated energy. A method for determining a set of optimum values of power and usage periods required by the model has also been developed. Furthermore, optimum values have been suggested that can be fine-tuned for use as benchmark values. The proposed approaches are the first of their kind.

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Section: Introductionmentioning

confidence: 99%