Data Analysis in the Energy Monitoring System Georgii Mikriukov 1 Perm National Research Polytechnic University Electrical engineering faculty Perm – Köhten

2  GridVis – Data acquisition software  OpenJEVis – Open source software, basis of energy monitoring system 2 Energy monitoring system

Functions of DAS DAS – Data Analysis System  Primary data processing  Data smoothing  Statistical parameters calculation  Energy consumption prediction  Atypical consumption detection 3 3

DAS functioning algorithm 4 E – raw data E’ – filtered data E* – smoothed data S – statistical data Ep – predicted energy consumption ci – clusters centroids ti – consumption time intervals fi – function of consumption ΔE = E’ – E* 4

5  Smoothing (moving average) 2N+1 – number of smoothing points (3, 5 or 7) x i – raw data point xs i – smoothed data point  Atypical consumption detection (ACD) ΔE < 0 – equipment switching off ΔE > 0 – equipment switching on 5 DAS functions: smoothing & ACD

6  Clusterization (k-means) V – standard deviation k – number of clusters x j – raw data point S i – cluster c i – centroid  Classification 6 DAS functions: clusterization & classification

7  Statistical parameters  maximal consumption  average consumption (per day, week, month etc.)  leveled consumption value (part of maximal)  Working time in different modes  Energy consumption prediction  0,75P av < P di <1,25P av – normal consumption  P di < 0,75P av – lowered consumption  P di > 1,25P av – heightened consumption P di – average consumption per day P av – average consumption for the whole period 7 DAS functions: statistics & prediction

8 Data analysis 8 Period of time:  Statistical parameters  Maximal consumption (Pmax): 2049,67 W· h  Average consumption (Pav): 352,03 W· h  Laboratory workload (P > 0,1Pmax): 54,91% Energy consumption, W · h

9 9 Atypical consumption detection ΔE < –600 – emergency event probability E’ &E*, W · h Δ E, W · h Period of time: Filtered (E’) and smoothed (E*) data ΔE = E’ – E* Period of time:

 3 main consumers:  lighting (70%)  lighting (30%)  laboratory stands 10 Data clusterization 2 3 =8 combinations

11 Energy consumer classification Class Lighting 70% Lighting 30% Standscici t1t1 t2t , , , , , , , ,14266 c i – cluster centroid (Y coordinate) 11

12 Energy consumption prediction weeks 3 weeks (more accurate) consumption, W · h Period of time:

Conclusion  Functions and structure of DAS has been considered  Statistics of one month ( ) has been analyzed  Energy consumers were classified according to clusterization data  Prediction curve of energy consumption has been calculated

Thank you for attention 14