Learning Phase at Head Ends

1. Learning Phase at Head Ends by Naoki ref: M. Baranski and V. Jurgen (2004) Implemented in Java with Java Genetic Algorithm Package (JGAP) by Josh Input Output Edge Events Appliance Table

2. Clustering Algorithm • Input • Edge events • Output • Clusters of on/off events time time time time • Procedures • Retrieve first event and search the rest for matching events by assigning the first event to a new cluster • Difference of power should be below threshold • Every time if finds a new matching event, update the power value of current cluster by averaging all values in the cluster • Assign on/off events with the same absolute power values to the same cluster • Calculate the mean and standard deviation of the cluster • Repeat the above procedures for the events that have not been assigned to any clusters yet by Josh

3. Clustering AlgorithmTest Result • TODO? by Josh

4. Appliance Table Building Algorithm GA • Input • Edge detection result • Clustering result • Output • Appliance state table • Steps • Selection of promising combinations of clusters • Initialization of FSM • Optimization of FSM DP by Naoki

5. Appliance Table Building AlgorithmStep1: Selection of promising combinations of clusters • Inputs • Edge events • Clusters eg) +100W, +50W, -150W, +1500W, -1500W • Intermediate Output: Matrix X (binary) • Column: each cluster • Row: promising combination of clusters eg) 1 1 1 0 0 +100W, +50W, -150W 0 0 0 1 1 X = ( ) +1500W, -1500W 1 1 1 1 1 +100W, +50W, -150W, +1500W, -1500W • Make combinations of clusters that compose state transitions • There are 2Nc combinations (Nc: # of clusters) • Impossible to examine all combinations when Nc is large • Select promising combinations by Genetic Algorithm • Sum of power values should be close to 0W +100W, +50W, -150W +100W, -1500W by Naoki

6. Appliance Table Building AlgorithmStep2: Initialization of FSM Valid Invalid +100W +50W -150W +100W -150W +50W +100W +100W 0W 100W 0W 100W 1 1 1 0 0 +100W, +50W, -150W 0 0 0 1 1 X = ( ) +1500W, -1500W 1 1 1 1 1 -150W +50W +50W -150W +100W, +50W, -150W, +1500W, -1500W 150W -50W • Select the best sequence pattern of clusters (Finite State Machines) • Assumption: each cluster (state transition) should appear exactly once • There are N1! permutations (N1: # of 1s in a row of X) • Impossible to examine all permutations when N1 is large • Put an upper limit on N1 in Step1 • Examine validity of each permutation • Powers should not be less than 0W in the middle of state transitions • Powers should not be 0W (off state) in the middle of state transitions • Powers should be 0W (off state) in the last state transitions by Naoki

7. Appliance Table Building AlgorithmStep2: Initialization of FSM (cont’d) Combination: +100W, +50W, -150W Valid sequences: +100W +50W -150W +50W +100W -150W • Select the best sequence pattern of clusters (Finite State Machines) • Make the best path by Dynamic Programming for each pattern • Properties used as the quality of each sequence • Time duration between state changes in a sequence • Deviation between the observed power value and the corresponding value of the cluster • Target value of each property is first set to the median of the all corresponding events • The closer to the target value, the better • Once the best path is created, update each target value with the median of the best path, and repeat the process until it fails to achieve better quality • Select the best sequence pattern • Frequent pattern is better • If the frequencies are the same, then select the pattern whose quality is the best by Naoki

8. Appliance Table Building AlgorithmStep3: Optimization of FSM 1 1 1 0 0 +100W +50W -150W 0 0 0 1 1 X = ( ) +1500W -1500W 1 1 1 1 1 +1500W +100W -1500W +50W -150W Solve overlaps 1 1 1 0 0 +100W +50W -150W X = ( ) 0 0 0 1 1 +1500W -1500W • Solve the overlaps of clusters • Assumption: each cluster (state transition) should appear for exactly one appliance • Select the best appliance based on the quality value among the appliances that share the same clusters (state transitions) • Recreate the finite state machines for non-best appliances without the overlapped clusters • If there are no valid sequences, then exclude the appliances by Naoki

9. Appliance Table Building AlgorithmTest Result 1 • Was able to build a correct appliance table • Confirmed that it can create multiple state FSM by Naoki

10. Appliance Table Building AlgorithmTest Result 2 • TODO? by Naoki

11. Re-learning Phase at Head Ends Day 1 Day 2 Day 3 Day 4 • Head end starts to build appliance tables • Upon requests from the meter (reactive) • Periodically (proactive) • Assumption: Similar appliance profiles should be observed over multiple days • Residents use the same appliances every day • Procedures • Examines the appliance tables created from multiple sets of data • If it finds an appliance whose state transition profile is different from that of the previously detected appliances, then it judges that a new appliance has been added • Ends the learning period when it does not find new appliances for a set period of time Power Power Power Power State # State # State # State # new appliance has been added by Naoki