Understanding Residential Occupant Cooling Behaviour through Electricity Consumption in Warm-Humid Climate
2.1. Environmental Stress Index (ESI) Analysis
2.2. Residential Energy Demand Monitoring: Auroville
2.3. Data Collection
- Blink meter-based monitoring: the electricity meter usually shows an alternating current (AC) static energy (Wh) consumption of the household, and there is an external light-emitting diode (LED) which “blinks” each time a certain amount of energy is consumed. The single-phase meters were of 16,000 blinks/kWh specification, i.e., 0.06 Wh/blink. In the case of three-phase meters, the blink resolution was 800 blinks/kWh (1.25 Wh/blink). Temporally precise electricity consumption was monitored in each candidate dwelling using blink meters mounted on top of the LED lights. Due to the characteristics of the blink meters, the blink depends on the amount of electricity consumed. For example, in the case of single-phase meters, the data are registered when at least 0.06 Wh was consumed, and one blink occurred; values lower than that would not be visible, and the system will wait to blink until it consumes a minimum 0.06 Wh. As a result, two days of the same household would have a different timestamp. The irregular timestamps made the aggregation or generalization of demand profile for further analysis a challenge without data transformation.
- Load shedding: there were regular instances of load shedding—deliberate shutdown in part or parts of a power-distribution system to relieve stress on an electricity grid when demand is higher than the supply—in the monitored buildings. Also, the frequency and duration of the load shedding were not homogenous. To counter the issue of load shedding, some buildings in Auroville uses on-site small diesel generators for electricity.
- Technical issues: the local cellular gateway was also subject to periodic interruptions due to load shedding, which caused gaps in the data collected. In some cases, the network was disconnected due to maintenance by the utility and service provider. The cellular network companies raised another challenge. There were two separate sim-cards for the buildings. As there was lack of specific data plans for the only Internet of things (IoT) systems, the SIM-cards were used with regular data plans, which automatically switch off the service after several months as there were no incoming or outgoing calls in those SIM-cards.
- Human factors: another major constraint was the human-related factors of people disconnecting power to the cellular gateway connected to the blink meters unknowingly, which caused missing data issues without notifying the project partners. In some cases, the residents of the monitored buildings connected temporary lights in shared spaces to the power plug for the cellular gateway. The project partners had to disconnect the temporary connections and restart the data collection.
2.5. Missing Data Filling
- Step 1—Recording missing values: This step involved recording missing data points in the raw data by assigning a timestamp and a “Nan” value.
- Step 2—Recording repeating values: Quite often, raw data contained repeating entries; our algorithm recorded up to 3 consecutive repeating values and removed them to retain a consistent equidistance time series.
- Step 3—Counting the total number of missing points: This step helped in assessing the overall quality check of raw data and was essential to account for the reliability of the analysis and results. For the present study, most of the dataset had a missing point within the 5–10% range.
- Step 4—Logical framework for replacing missing values while retaining periodic components: this was achieved as part of a logical argument that electricity consumption patterns remained consistent (with a small element of randomness) over the different weeks due to similar routines and weekly activity patterns. For example, the consumption at a specific time of the day (say, 9:00 a.m.) for a specific day type (say, Monday) should fluctuate within the close vicinity to values noted at the similar time and day type on following or a previous week. The logical framework was implemented as an iterative procedure.
- Step 5—Infilling missing values in week 1: this step strategically replaced the missing values in the first week using the data from the following week. The missing value was maybe also missing in the 2nd week. In such cases, the algorithm was designed to move forward and scan week 3 to find the relevant value. The process was repeated until all missing data points are infilled in week 1.
- Step 6—Infilling missing values from Week 2 to the end of the dataset: this step replaced the missing values using the data from the preceding weeks. Following the same analogy as in Step 5 if the missing value was also missing in the subsequent week. In such cases, the algorithm was designed to move forward and scan the week after to find the relevant value. The process was repeated until all missing data points were infilled in the week.
- Step 7—Ensuring all missing data points were infilled: to ensure all the missing values were infilled in step 5 and 6, the algorithm checked the total number of Nan values. If there were remaining missing values, step 5 and 6 were iteratively run for up to 10 runs (depending on the length of the data set). The iterative process automatically stopped when all the missing data were infilled.
- Step 8—Final checks: at the end of the iterative process, the algorithm printed the total number of missing values left after ten iterations. Step 8 was essential as in some rare cases, even after ten iterations, some missing points remain. This happened when the value for a particular time/day was consistently missing throughout the dataset. In such cases, users were informed with the remaining number of missing data points which could be infilled by the user (case-by-case) using some other techniques/logical basis and depending on the nature of the study.
3. Results and Discussion
3.1. Climate: Auroville
3.2. Electricity Consumption Pattern in Monitored Households with Single-Phase Meters
3.3. Developing a Weekly Profile without Missing Data
3.4. Electricity Consumption Analysis for Comfort in the Selected Household
Conflicts of Interest
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Debnath, K.B.; Jenkins, D.P.; Patidar, S.; Peacock, A.D. Understanding Residential Occupant Cooling Behaviour through Electricity Consumption in Warm-Humid Climate. Buildings 2020, 10, 78. https://doi.org/10.3390/buildings10040078
Debnath KB, Jenkins DP, Patidar S, Peacock AD. Understanding Residential Occupant Cooling Behaviour through Electricity Consumption in Warm-Humid Climate. Buildings. 2020; 10(4):78. https://doi.org/10.3390/buildings10040078Chicago/Turabian Style
Debnath, Kumar Biswajit, David P. Jenkins, Sandhya Patidar, and Andrew D. Peacock. 2020. "Understanding Residential Occupant Cooling Behaviour through Electricity Consumption in Warm-Humid Climate" Buildings 10, no. 4: 78. https://doi.org/10.3390/buildings10040078