Version 1
: Received: 20 September 2023 / Approved: 21 September 2023 / Online: 21 September 2023 (11:30:58 CEST)
How to cite:
Okpala, C.; Njoku, H.; Ako, P. A Data Envelopment Analysis to Benchmark Hotel Energy Consumption in an Urban Locality. Preprints2023, 2023091476. https://doi.org/10.20944/preprints202309.1476.v1
Okpala, C.; Njoku, H.; Ako, P. A Data Envelopment Analysis to Benchmark Hotel Energy Consumption in an Urban Locality. Preprints 2023, 2023091476. https://doi.org/10.20944/preprints202309.1476.v1
Okpala, C.; Njoku, H.; Ako, P. A Data Envelopment Analysis to Benchmark Hotel Energy Consumption in an Urban Locality. Preprints2023, 2023091476. https://doi.org/10.20944/preprints202309.1476.v1
APA Style
Okpala, C., Njoku, H., & Ako, P. (2023). A Data Envelopment Analysis to Benchmark Hotel Energy Consumption in an Urban Locality. Preprints. https://doi.org/10.20944/preprints202309.1476.v1
Chicago/Turabian Style
Okpala, C., Howard Njoku and Paul Ako. 2023 "A Data Envelopment Analysis to Benchmark Hotel Energy Consumption in an Urban Locality" Preprints. https://doi.org/10.20944/preprints202309.1476.v1
Abstract
The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.
Keywords
hotel energy consumption, data envelopment analysis, hotel benchmarking, building energy efficiency
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
