Author:Giacomo Ghidini, Sajal K. Das

1. Improving Home Energy Efficiency with E2Home: A Web-based Application forIntegrated Electricity Consumptionand Contextual Information Visualization Author:Giacomo Ghidini, Sajal K. Das IEEE SmartGridComm 2012 Symposium - Smart Grid Standards, Testbeds and Field Trials Symposium

2. Outline • INTRODUCTION • Related work. • Our contributions • E2HOME • Data collection • Data fusion • Data visualization • EXPERIMENTS AND DISCUSSION • CONCLUSIONS

3. 1.INTRODUCTION • Applications in diverse domains such as traffic and transportation[1], smart environments [2], health and well being[3], fitness and sports [4], games and entertainment [5],and social networking [6] have benefited from advances in pervasive computing and communications. • these application scenarios have two important featuresin common: (i) diverse data sources, and (ii) lay (application) users. In this paper, we argue that pervasive computing and communications can also benefit the smart grid by enabling utility customers to play an active role in lowering the overall electricity demand.

4. Data sources and users. In smart environments, relevant information is distributed across many data streams from diverse and independent sources. A smart environment application should fuse the relevant data from all relevant sources, and present them to lay users in a fashion such that makes it easy for them to explore the data and quickly obtain the necessary information.

5. Electricity prices . Average electricity prices have risen overthe last 14 years for all classes of customers. Residential and commercialcustomers have been hit the most with increases close to 50%.

6. A. Related work • Following a greater interest in energy efficiency, several pilot projects were rolled out across the U.S.A., including one by CenterPoint Energy and the U.S. Dept. of Energy ; and another one by Alliant Energy and IBM . • One of the objectives of these projects is to analyze the current consumption behavior of customers, and investigate what role they can/want to play in reducing electricity consumption and thus increase energy efficiency.

7. Related work • U.S. DoE Energy Efficiency and Renewable Energy,IBM seem to suggest that pervasive technologies can help raise awareness of electricity consumption among customers. For instance, 83% of participants reported changing their consumption behavior once consumption data were made available to them via in-home displays (IHDs)

8. Related work • More recently, a public-private partnership supported by the National Institute for Standards and Technology has developed Green Button Data , a standard for the distribution of electricity usage data. • This standard will further ease the development of applications that fuse electricity usage data with other third-party data streams to offer an insightful view into consumers’ energy usage.

9. Related work • Google and Microsoft released PowerMeter and Hohm , respectively. • These are Web-based applications that let customers access their electricity consumption data and visualize them on interactive charts. (Smart Meter Texas offers a similar service , although the charts are not interactive.) Notwithstanding support from Google and Microsoft, both projects are being discontinued due to interest below expectations.

10. Google PowerMeter . An interactive visualization displays theelectricity consumption on a time chart. Although the user can navigate the time chart to observe recorded energy consumption and associated cost, aswell as comparison between current consumption and cost with past usage,no contextual information is provided for the data, thus making it difficultto point out wasteful consumption and take actions to change behavior toimprove energy efficiency.

11. Related work • We argue that the limited utility in reducing the energy consumption is one of the reasons why these applications have not attracted more users. Neither Google PowerMeter,nor Microsoft Hohm, nor Smart Meter Texas offer a way to answer questions such as: • Where (i.e., home, not home) was I when this high energy consumption occurred? • What was the weather like (e.g., heat wave) when this high energy consumption occurred?

12. Related work • If the answers are that the resident was home and a heat wave was in progress, then even a high electricity consumption(likely due to A/C) might be acceptable, and thus the user does not need to take specific actions. • On the contrary, actions might be needed if the answers to these questions imply wasteful energy consumption as in the case when the user is not home and the outdoor temperature is mild. • Translated into pervasive computing and communications terms, these applications do not provide context information for the electricity consumption data.

13. B. Our contributions • Based on our analysis of existing solutions, we developed Energy-Efficient Home (E2Home), a system in which contextual information such as the resident’s location is fused with the electricity consumption data into a multidimensional data stream, thus providing the user with actionable information on which to act to reduce energy consumption.

14. 2. E2HOME • E2Home is built on top of FuseViz [15], a framework for data fusion and visualization in pervasive computing environments. • For the implementation of E2Home, we opted to rely on the FuseViz framework because it offers support for fusion and visualization of large, live, heterogeneous, and independent data streams

15. Architecture of E2Home application. Electric energy consumptionand user’s location data are stored in CouchDB databases and then fusedinto a joint data stream for each 15-min time slot. The fused data are thenvisualized using a focus and context time line (average power consumptionvs. time) and a map (user location).

16. E2HOME • Other ReSTful (i.e., complying with the representational state transfer model ) data storage systems such as Sensor.Network , and Pachube provide APIs to create, read, update, and delete data streams, but lack built-in support for data fusion of independent data sources. • E2Home is a FuseViz-based Web application, which consists of data collection, storage, fusion, and visualization.

17. A. Data collection • A Python script downloads electricity usage data from the SMT Web site [14] and posts them to the CouchDB [19] data stream database. The Python script periodically accesses the Web site with the user’s credentials, and downloads a CSV (comma-separated value) file with the electric energy consumption data. • The downloaded CSV file is then processed into a JSON document conforming to the CouchDB data stream validation function, and uploaded to the CouchDB database via HTTP. Clearly, the data collection operation would be much simpler and secure, if SMT offered a ReSTful API, and supported de facto standard authentication mechanisms such as OAuth [20].

18. Data collection • An Android app on the user’s smartphone tracks the location and saves it to the CouchDB data stream database. • If Internet access is not available, location data are temporarily stored on the device, and then uploaded whenever a connection is available.

19. JSON documents for E2Home app. A MapReduce function fusesmultipleindependent energy consumption (a) and user location (b) documentsover 15-minute time slots to generate a joint data stream document such asthe one in (c).

20. CouchDB requires _id and _rev fields in the JSON documents.Additional common fields are type and timestamp.The first one is used to easily distinguish between energy and location data points in the MapReduce function, while the latter stores the time instant to which the data point refers. • Since the two data streams are unsynchronized, non-trivial processing is needed to fuse them into a joint data stream ready for visualization.

21. B. Data fusion • The two data streams are replicated to a common CouchDB database for which a view and a list function have been defined. In the view, there exists only the Map part of the MapReduce function. • This takes data points of either data stream as input and indexes them by timestamp. The list function reads the ordered data points one by one and groups them into k-min time slots.

22. It then computes (i) the average power consumption (measured in kW) over that time slot from the sum of all energy consumption (measured in kWh) data points over the same period; and (ii) the location centroid from all the locations recorded for that time slot. Finally, it outputs the data point for the joint data stream as depicted in Fig. 5(c).

23. C. Data visualization • In its current version, the E2Home application consists of a Web page with two charts on which data points from the live joint data stream are plotted: (i) a focus and context time line for power consumption, and (ii) a map for the corresponding user’s location.

24. Fig. 6. Panning and zooming on E2Home data. The user can select atime interval (gray area) on the time chart, and then observe the detailed datawithout losing the context. Sliding the selection helps observe the variationover time of average power consumption on the time chart and location onthe map. (Similarly, the user can zoom to a specific area on the map, and panthe selection.)

25. Brush-and-linking on E2Home data. Thanks to brush-and-linkingthe user can point out situations when he/she was not home and high levels of average powerconsumption were recorded (blue data points > 2 kWin top chart). The E2Home application helps narrow down wasteful energyconsumption, unlike Google PowerMeter, Microsoft Hohm, and Smart MeterTexas.

26. 3. EXPERIMENTS AND DISCUSSION • Electric energy consumption 08/24/2011 – 09/30/2011. The joint data stream accounts for 30 days and 13 hours instead of 38 days because the user location was not available for several short time periods when the smartphone was turned off. The (Not home, > 2 kW) and (Not home,<=2 kW) sets contain unselected data points (blue) in Fig. 7 with average power consumption above 2 kW, and below 2 kW, respectively; the (Home) set contains selected data points (red).

27. Observed energy consumption 08/24/2011 – 09/30/2011. Whilethe 32 data points with average power consumption above 2 kW when theuser is not home cover only 1:09% of the time, they account for 11:34% ofthe energyconsumption. If this information is presented to the user via thevisualizations in Fig. 7, he/she can take necessary actions to reduce the energyconsumption associated with these relatively few data points, thus having amajor impact on the overall energy efficiency of the home.

28. Google PowerMeter [12] and SMT [14] cannot help the user identify times when high power consumption occurred when he/she was not home, because they rely only on smart meter data. • Instead, using our E2Home application the user has access to information about times when he/she was not home and high energy consumption was recorded. • Using this insightful information, the user can adjust his/her behavior and take actions to curb this wasteful consumption. If the user is successful at this task, high energy consumption when nobody’s home will be greatly reduced in the future.

29. To compute the projected energy consumption for the following 38-day period,we substitute the 0.772 (kWh / data point) average energy consumption of the 32 high energy consumption data points with the0.032 (kWh / data point) average energy consumption of the 1, 530 low energy consumption data points. • As depicted in Fig. 9, the total energy consumption for these 32 data points decreases from 24.71 kWh to 1.034 kWh, thus bringing about a projected overall energy efficiency improvement of 10.86%.

30. Projected energy consumption 08/24/2011 – 09/30/2011. If theuser reduces the energy consumption associated with the 32 data points withaverage power consumption above 2 kW when he/she is not home, thenthe overall energy consumption for a 38-day period goes from the observed217:965 kWh to the projected 194:289 kWh, which corresponds to an energyefficiency improvement of10:86%.

31. The projected energy efficiency improvement can be directly computed in the E2Home app, and be presented to the user.Clearly, additional data sources such as appliance-specific meters would further enhance the quality of the information in terms of where the consumption occurred, thus resulting in a more accurate estimation of the achievable energy efficiency improvement.

32. 4. CONCLUSIONS • In this paper, we present a novel application, called E2Home, for the fusion of electricity consumption data and residents’ locations. The fused data stream is then visualized on interactive charts and maps, thus providing the user with actionable information useful to change their behavior and reduce electricity consumption. Projected energy efficiency improvement exceeds 10%.

33. We are currently working on the incorporation of other data streams, such as weather information and appliance-specific electricity consumption data, in the E2Home application. We are also performing an experimental study at 12 campus apartments to quantitatively assess the impact of E2Home on the electricity consumption behaviors of residents. • Finally, we are migrating from Smart Meter Texas to Green Button Data as the source for electricity usage data. Being a NIST-supported nationwide standard, Green Button Data will enable a larger audience to use E2Home for their electricity usage analysis and forecast.