Multi-rate Event Detection for Energy-Aware Green Design Facilities	
	
                            Amrita Anand	          Kedar Mane
              Electrical & Computer Engineering	          Electrical & Computer Engineering
                   Carnegie Mellon University	           Carnegie Mellon University
                         Pittsburgh, PA 15213	             Pittsburgh, PA 15213
                    amritaa@andrew.cmu.edu	             kmane@andrew.cmu.edu	
Rathi Munukur
Electrical & Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
rathim@andrew.cmu.edu
	
Abstract
          The buzz word in today's world is energy and energy management.  One step to efficiently conserve energy is to first find the energy consumption by each appliance and then accordingly take steps to decrease the overall energy consumption. The objective of ”Multi-rate Event Detection for Energy-Aware Green Design Facilities” is to detect the occurrence of events (on-off of appliances) in order to estimate the distribution of power consumption. This is an extension of “A Framework for Enabling Energy-Aware Facilities through Minimally-Intrusive Approaches” (thesis of Mario E. Berges) which aims at non-intrusive load monitoring i.e., load monitoring without the usage of sub meters. Apart from using the Generalized Likelihood Ratio algorithm, techniques such as multi-rate sampling are explored to improve accuracy of the detected events. Different signatures are created to characterize the possible events. These signature libraries are used to assist the detection of events by performing signature matching. This improves the accuracy of the detected events by eliminating false detections. An improvement of around 5% in the number of correct events detected was obtained while using multiple sampling as opposed to using a single sampling rate.

1	Introduction
What are our energy needs today? What will be our energy needs tomorrow? Current trends show that total world consumption of marketed energy increases by 49 percent from 2007 to 2035. Almost 73% of the energy that reaches the end user is consumed by the residential sector and buildings. With an ever increasing demand for energy, strategic plans are being formulated worldwide to manage our energy needs. Providing the end user with detailed data about their day-to-day power utilization with respect to specific appliances will help them take steps to reduce their power consumption.
The objective of ”Multi-rate Event Detection for Energy-Aware Green Design Facilities” is to correctly classify and detect electrical loads from a centralized location in a building’s power distribution system instead of from sub meters. As an extension to the Generalized Likelihood Ratio algorithm, multi-rate sampling is explored to improve accuracy. Signature libraries were created to characterize the events and were used to reduce false positives.

1.1	Motivation 
Apart from all the recent energy policies from organizations such as LEED- (the Leadership in Energy and Environmental Design):
•	leveraging ‘People Power’ is of utmost importance.
If consumers are given access to detailed data about their day-to-day power consumption they can: 
•	make informed decisions towards managing their own demand.
•	achieve huge cost savings.
•	reduce overall energy consumption.
Our method not only achieves the above, but:
•	reduces cost borne by consumers further by avoiding the use of sub-meters.
•	can also be used in applications like  surveillance.

1.2 	Background
Our final aim is to facilitate a convenient means of two way communication between the power supplier and the power consumer. The user would have an interface to monitor details about the power consumption at his house including details such as which appliance consumed how much energy at what time of the day. 
Conventional techniques include using submeters wherein each appliance is fitted with a submeter to monitor energy consumption of that specific equipment. Our approach is towards having a more centralized means of monitoring appliances in order to bring down the equipment costs as well as installment cost burdens that the consumers have to bear.
Other similar approaches [1],[2] have been made wherein a single sampling frequency was used in detecting events. Our approach explores improving these techniques by using multi rate event detection which not only considers a combination of different sampling rates but also modifies other parameters such as detection window, pre event and post event averaging windows which are a function of the sampling frequency.

2	Event detection using GLR
The GLR detection algorithm calculates a decision statistic from the natural log of a ratio of probability distributions before and after a potential change in mean.
 
2.1 	Detector characteristics
A detector is created. The characteristics specified are as follows and these are also a function of the sampling frequency being used:
•	Sampling frequency:
•	This is the sampling rate at which the signal was collected.
•	Detection window: 
•	This is the window within which the strongest event is detected. 
•	Detection window size=4* Sampling rate
•	Pre-event and Post-event averaging window:
•	These windows are used to evaluate the characteristics of the signal before and after every point in the signal to determine if there was an event at that point.
•	Pre-event window size=Post-event window size=1.5* Sampling rate
•	Minimum power level:
•	This is the minimum power differential allowable for an event given in Watts.
•	Minimum vote count:
•	This defines the number of votes needed to consider that point as an event.
•	Median filtering order
•	This is the order of the median filter used on the input power signal.

2.1 	Steps involved

We use datasets measured at a particular house. The dataset contains centralized power readings of the house in terms of real and reactive power usage. It also contains readings that were manually marked as the on and off events of appliances with the timestamps including the transitions. The steps involved in detecting the transitions once the detector is created are:
•	The signal is passed through a median filter to remove any spikes due to noise.
•	The signal is traversed through using the pre and post event windows. The log likelihood ratio is found at each point. The llr is calculated only if the power change is greater than the minimum power transition that can be considered as an event.
•	The signal is now transformed into a sequence containing the log likelihood ratios of each corresponding point.
•	The detection window is slid across this signal (traversing by one point at a time). In each window, the highest log likelihood ratio is identified and its position is noted.
•	If that point was identified as an event at instances greater than a minimum, then it is noted as an event.

3	Signature Libraries
We have used two different approaches to create signatures that would characterize the different appliances in use and also their various state transitions:
•	Delta means:
We use the delta of the pre-event and the post-event averages. The averages are taken over some period of time apart from the exact occurrence of the event. This helps us get the means of the event transients that are present, thus helping us identify the event with more accuracy. The reason for taking the delta is that then events can be detected even when there are other previously running appliances. 
•	Transient power weights:
We also use feature vectors for every application where in we consider weights in terms of cosine and sine of the power elements on the respective phases the appliance ran on. These include both real and reactive power elements. A certain range is again considered to reel in the transient effects.

4	Multirate event detection
In the multirate event detection algorithm not only is the used sampling rate at every iteration being changed, but also the input parameters to the event detection algorithm that are a function of the sampling frequency, are automatically being modified too. With this happening, the pre event, post event and detection window sizes go on decreasing with reduction in the sampling frequency. As these windows are parsed through the power signal to detect events in the case of large window sizes, the event with a weaker change is overshadowed by neighboring stronger events. Therefore as it is the strongest event within a window that is noted, changing the window size influences the events that are identified. Hence events detected at one sampling rate might not have been detected at some other sampling rate.
Thus balancing the tradeoffs of using both higher window sizes and smaller window sizes in an intuitive way gives the method an edge over just using a single sampling rate, thus detecting events much more accurately. 

4.1	Combining results
The original signal is downsampled and the event detection procedure is followed for each sampling rate. The events found at each sampling rate are combined by ORing.
While using multirate sampling, there is a possibility that the same event might be detected at different neighboring points. Hence ORing of events will result in viewing one event as multiple events. In order to prevent this, after the direct combination of events, filtering is done to account for only one event in situations where there is a cluster of very closely spaced detected events.

5	Signature matching

Signature matching is being used at the very end of the algorithm. This is done in order to get rid of the false positives that would turn up after combining the events detected from multiple sampling rate iterations. We create feature vector signatures for every detected event and compare it with the signature libraries we have created for the actual events. The Euclidean distance between the calculated signature and each signature in the library is found. If the minimum of these calculated distances is lesser than a threshold then the event is taken as a valid event. Otherwise, it is rejected as a false positive. This threshold was calculated from the parameters of the given dataset. 

6	Verification of results
In order to test the accuracy of this detection procedure, the detected events were compared with the dataset that was obtained by manually marking the events on the same timeline. Since the events were marked manually, we did observe that there were instances where an event was missed during the manual marking but detected by our algorithm.

7	Results
The results obtained while using one of the data sets are show below:
Sampling rates (Hz)	20	20 & 10	20, 10 & 5	20, 10, 5 & 4	
Correct events detected	197	198	201	206	
False positives reduction	250	341	436	506	
This shows that there is an increase of 4.6% in the number of events detected.

This is how the confusion matrix looks like for the same dataset:

	Positive	Negative	
Positive	205	170	
Negative	335	0	
Acknowledgments
We would like to thank the following people for their continuous motivation, encouragement and help at every step of our project:
Prof Mario Berges (CMU CEE), Prof Bhiksha Raj (CMU LTI,ECE).
References
 [1]   Dong Luo, Leslie K. Norford, Steven R. Shaw, & Steven B. Leeb (2002) Monitoring HVAC Equipment Electrical Loads from a Centralized Location – Methods and Field Test Results. ASHRAE Transactions 2002.
[2]   Mario E. Berges Gonzalez (2010) A Framework for Enabling Energy-Aware Facilities Through Minimally-Intrusive Approaches. Carnegie Mellon University Thesis.