Dataset on information strategies for energy conservation: A field experiment in India

The data presented in this article are related to the research article entitled: “Information strategies for energy conservation: a field experiment in India” (Chen et al., 2017) [1]. The availability of high-resolution electricity data offers benefits to both utilities and consumers to understand the dynamics of energy consumption for example, between billing periods or times of peak demand. However, few public datasets with high-temporal resolution have been available to researchers on electricity use, especially at the appliance-level. This article describes data collected in a residential field experiment for 19 apartments at an Indian faculty housing complex during the period from August 1, 2013 to May 12, 2014. The dataset includes detailed information about electricity consumption. It also includes information on apartment characteristics and hourly weather variation to enable further studies of energy performance. These data can be used by researchers as training datasets to evaluate electricity usage consumption.


Specifications
Economics, Engineering, Psychology More specific subject area

Energy, Consumer Behavior, Smart Grid
Type of data Experimental data How data was acquired The electricity use data was collected at the field site by direct measurement during the period from August 1, 2013 to May 12, 2014. Apartment and household characteristics were collected through a survey that was completed by each participating household at the beginning of the experiment. The weather data was obtained from the weatherunderground.com from the Indira Gandhi International Airport.

Data format
Stata .dta files Experimental factors The dataset includes information about electricity consumption, weather, apartment, and household characteristics.

Experimental features
Aggregate electricity consumption was measured for each participating household. The sampling interval for the high-frequency data is every thirty seconds but has been aggregated to fifteen minute, thirty minute, hourly, and daily measurements. The weather variables update hourly from station measurements.

Value of the data
Features household high-frequency electricity data consumption at 15 min intervals for 10 months. Data among the highest resolution available to-date in a behavioral experiment in a developing country.
Measures behavioral responses to information about electricity consumption.

Data
The data described in this article was acquired 24 h a day in a field experiment at Indraprastha Institute of Information Technology in New Delhi, India during the period from August 1, 2013 to May 12, 2014. The data are related to the research article entitled: "Information Strategies for Energy Conservation: A Field Experimentenergy conservation: a field experiment in India"(Victor L. Chen, Magali A. Delmas, Stephen L. Locke, Amarjeet Singh, 2017). [(Chen et al., 2017) [1]. The raw data includes (i) time stamp; (ii) electricity consumption in kilowatt-hour (kWh) per unit time; (iii) weather data; (iv) engagement with the treatment messages and online energy dashboard; and (v) apartment dwelling and occupancy characteristics, which do not vary with time during data collection. For convenience, the data is provided in panel format for time series analysis. For time varying variables, each successive row represents a 15 min, 30 min, hour, or daily increment.

Materials and methods
The baseline period for all metered apartments was approximately lasted from August 1, 2013 to February 18, 2014. During this baseline period, no behavioral interventions were performed. The treatment interventions began on February 19, 2014 and the treatment groups were sent weekly emails about their electricity usage and were given access to an online electricity dashboard where they could monitor their electricity usage in real time.

File structure
A Stata.dta file (version 12) for each table has been included. If a table uses data measured at different frequencies or includes a different number of observations, a file has been created for each table and column. For example, Table_2.dta can be used to replicate all of Table 2 while Table_6_1.dta can be used to replicate column 1 of Table 6. A corresponding Stata.do file has been included that will load the appropriate dataset and replicate the specified table. Table 1 describes the variables in each dataset. The frequency of data can be determined by looking at the timestamp. Week of experiment