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

Input – Edge events Output – Clusters of on/off events Clustering Algorithm 2 time by Josh 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

Clustering Algorithm Test Result 3 by Josh TODO?

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

Inputs – Edge events – Clusters eg) +100W, +50W, -150W, +1500W, -1500W Intermediate Output: Matrix X (binary) – Column: each cluster – Row: promising combination of clusters Appliance Table Building Algorithm Step1: Selection of promising combinations of clusters 5 – Make combinations of clusters that compose state transitions – There are 2 Nc 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 by Naoki X = ( ) W, +50W, -150W +1500W, -1500W +100W, -1500W W, +50W, -150W, +1500W, -1500W eg)

Appliance Table Building Algorithm Step2: Initialization of FSM 6 – Select the best sequence pattern of clusters (Finite State Machines) – Assumption: each cluster (state transition) should appear exactly once – There are N 1 ! permutations (N 1 : # of 1s in a row of X) – Impossible to examine all permutations when N 1 is large – Put an upper limit on N 1 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 0W 100W 150W +100W +50W-150W 0W 100W -50W +100W -150W+50W ValidInvalid by Naoki X = ( ) W, +50W, -150W +1500W, -1500W W, +50W, -150W, +1500W, -1500W +100W +50W -150W+100W -150W +50W

Appliance Table Building Algorithm Step2: Initialization of FSM (cont’d) 7 by Naoki +100W, +50W, -150W +100W +50W -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 +50W +100W -150W Combination: Valid sequences:

Appliance Table Building Algorithm Step3: Optimization of FSM 8 – 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 X = ( ) W +50W -150W +1500W -1500W +1500W +100W -1500W +50W -150W Solve overlaps X = ( ) W +50W -150W +1500W -1500W

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

Appliance Table Building Algorithm Test Result 2 10 – TODO? by Naoki

Re-learning Phase at Head Ends 11 –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 by Naoki Power State # Day 1 Power State # Day 2 Power State # Day 3 Power State # Day 4 new appliance has been added