Data supporting the improvement of forecasting and control of electricity consumption in hotels

Improving and managing the electricity efficiency in hotel facilities is essential to reduce the hotel operation costs and its environmental impacts. The data presented shows the evolution of the electricity consumption and management between 2013 and 2015 in two hotel facilities in Cuba (one beach hotel and one city hotel). The data additionally includes the daily measures used to develop control tools for an energy management system. The data presented in the article relates to the research study: Tools to improve forecasting and control of the electricity consumption in hotels Cabello et al., 2016, and it corresponds to the energy audits developed in one beach hotel (Hotel A) and one city hotel (Hotel B) in Cuba.


a b s t r a c t
Improving and managing the electricity efficiency in hotel facilities is essential to reduce the hotel operation costs and its environmental impacts. The data presented shows the evolution of the electricity consumption and management between 2013 and 2015 in two hotel facilities in Cuba (one beach hotel and one city hotel). The data additionally includes the daily measures used to develop control tools for an energy management system. The data presented in the article relates to the research study: Tools to improve forecasting and control of the electricity consumption in hotels Cabello et al., 2016, and it corresponds to the energy audits developed in one beach hotel (Hotel A) and one city hotel (Hotel B) in Cuba.

Data
The data includes the occupied rooms per day (ORD), the outdoor temperature and the electricity consumption on daily and on monthly basis. It additionally includes the parameters calculated and used to develop the electricity management of the hotel facilities (i.e. room degree-day (RDD), energy performance indicators (EnPI), energy baselines (EnB) and control graphics), to forecast and control the electricity consumption in hotels A and B, by highlighting their main sources of inefficiencies. Originally, the dataset [1] considered data from 2011 to 2012 to develop the EnBs and EnPIs used. This data was updated up to 2015 to show the evolution of the electricity management system and the control tools over time. Fig. 1 and Fig. 2 show the average electricity consumption of the different areas for hotels A (Fig. 1) and B (Fig. 2). Moreover, Fig. 3 and Fig. 4 show the electricity consumption between 2011 and 2014 for both hotels, and additionally show the Energy Performance Indicators (EnPI) developed for hotels A and B. Furthermore, the daily control graphs, developed for each month based on the daily electricity consumption measured in each hotel are shown in Fig. 5 and Fig. 6. Finally, Fig. 7 shows a scatter analysis between the measured and the forecasted electricity consumption in hotels A and B. In addition, Table 1 shows the monthly electricity consumption measured between January 2011 and December 2014 and the reference parameters calculated based on the measurements. Moreover, Table  2 shows the monthly electricity consumption measured during 2015 and the reference parameters calculated based on the measurements. The data used to develop Figs. 1e6 is available in the article.

Experimental design, materials, and methods
The monthly electricity consumption was taken from the electric bills of each hotel, while the daily consumption was taken from the mandatorily measures take every day at 7 a.m. in the electric meter of every hotel by the maintenance staff, as requested by the Cuban Ministry of Tourism to keep track of the electricity consumption of the tourist sector. Other data (e.g. occupied rooms per days, occupied rooms per month, etc.), was taken from each hotel records.
The electricity consumption per areas used to identify the main electricity uses (as depicted in Figs. 1 and 2), was measured with two IP power meter of four channels to measure 4 areas simultaneously Specifications  [1].

Value of the data
The dataset can be used as starting point to forecast and manage the electricity consumption in Cuban hotels. The dataset can be used, as benchmark to compare the behavior of hotel facilities in tropical areas, for specialists in the energy management of buildings.
The dataset can be used as a reference to benchmark the energy performance of hotels in tropical areas.
The data can be used in courses of energy efficiency and management in buildings, as a detailed example of the implementation and evolution of the energy efficiency and management measures in hotel facilities. The data serve as a guidance to target specific areas and equipment during a measurement campaign in hotel facilities. The data shows the evolution of the electricity consumption of two hotel facilities during the implementation of energy efficiency measures within an energy management system.      with each one. Additionally, a power quality and energy analyzer Fluke 435 series 6 was used. Moreover, the electricity consumption of the hotel was directly taken from the hotel electric meter. The areas on each hotel were measured during one month, Figs. 1 and 2 shows the average values. Similar to Ganguly [4], the climatic year (i.e. a continuous 12-month period with a complete annual cycle), developed using 30 years of daily temperature data [5], available in the Weather Underground Database [6], was used to forecast the daily outdoor temperature. The Room Degree Day (RDD) is calculated as: where CDD stands for Cooling Degree Day, which is calculated as [7]: where ∅ 0 is the outdoor temperature, and ∅ b is the reference temperature (maximum outdoor temperature at which no cooling is required to maintain the thermal comfort in a building). The reference temperature must be individually determined for each building [7]. The monthly electricity consumption was forecasted during 2013 and 2014 using the correlation between the electricity consumption and the RDD for hotels A (equation (3)) and B (equation (4) Fig. 7 shows a scatter analysis between the measured and forecasted electricity consumption in hotels A and B.
Equations   Table 1 shows the monthly electricity consumption and the reference parameters between January 2011 and December 2014, while Table 2 shows the same data measured and calculated during 2015, when the updated control tools, depicted in figures Figs. 3e6 were implemented. Additionally, Table 2 includes the electricity consumption forecasted with the updated EnPI and the monthly reference parameters during 2015.