Presentación realizada en el marco del Proyecto PINV18-661: Análisis de la eficiencia energética en edificios no residenciales mediante técnicas metaheurísticas y de inteligencia artificial.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Daumas, Federico

Diego P, Pinto-Roa

Divina, Federico

García-Torres, Miguel

Gardel, Pedro Esteban

Gómez-Vela, Francisco

Medina, Hernán

Morales, Félix

Román, Federico

Sauer, Carlos

Velázquez, Gustavo

Vázquez Noguera, José Luis

English

Repositorio Institucional CONACYT

Parallel Evolutionary Biclustering of Short-Term Electric Energy ConsumptionPINV18-661Diego P. Pinto-Roa, Hernán Medina, Federico Román, Miguel García-Torres, Federico Divina, Francisco Gómez-Vela, Félix Morales, Gustavo Velázquez, Federico Daumas, José L. Vázquez Noguera, Carlos Sauer, Pedro Gardeldiego.pinto@ua.edu.py/dpinto@pol.una.py1Agenda➜ Short-term electric energy consumption➜ Bi-clustering➜ Evolutionary Bi-clustering➜ Parallel Evolutionary Bi-clustering➜ Simulation➜ Conclusion and Futures Works2Short-term electric energy consumption3Energy consumption of buildings is receiving more and more attention in today’s economies.05101520252000 2010 2020 2030 2040 20502020history projectionstotal buildings sector delivered energy consumptionresidential buildingscommercial buildingsAnual Energy Outlook 2021Buildings delivered energy consumptionAEO2021 Reference casequadrillion British thermal units 4Short-term Electrical Energy ConsumptionJacob, M., Neves, C., & Greetham, D. V. (2020). Short term load forecasting. In Forecasting and Assessing Risk of Individual Electricity Peaks (pp. 15-37). Springer, Cham.• The short-term load forecastis for a period of one day toone week.• Short term load forecastingcan help in estimating loadflows and making decisionsthat can prevent overloading.5Short-term Electrical Energy Consumption1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39Time SeriesElectric Energy Consumption SamplesX1X2X3X4X56Short-term Electrical Energy Consumption1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39Time SeriesElectric Energy Consumption SamplesX1X2X3X4X5b1b2b2b3b37Bi-clusteringis a complex task of Machine Learning8Bi-Clustering vs. Clustering9Data Matrix Clustering by row Clustering by columnObservationFeaturesKameshwaran, K., & Malarvizhi, K. (2014). Survey on clustering techniques in data mining. International Journal of Computer Science and Information Technologies, 5(2), 2272-2276.Bi-Clustering vs. Clustering10Data MatrixObservationFeaturesClustering by rows Clustering by columns in the rowTow-way ClusteringKameshwaran, K., & Malarvizhi, K. (2014). Survey on clustering techniques in data mining. International Journal of Computer Science and Information Technologies, 5(2), 2272-2276.Bi-Clustering vs. Clustering11Hartigan, J. A. (1972). Direct clustering of a data matrix. Journal of the american statistical association, 67(337), 123-129Data MatrixObservationFeaturesBi-clusteringFind submatricesGoal of bi-clustering is Identify homogeneous submatricesBi-cluster of a Short-term Electrical Energy Consumption1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39Time SeriesElectric Energy Consumption SamplesX1X2X3X4X5b1b3b31 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40X5 46 42 45 43 44 50 42 41 44 48 42 50 48 45 43 46 48 50 47 49 46 46 46 41 47 46 43 49 43 41 45 50 49 46 44 44 44 43 42 41X4 31 31 37 38 39 40 37 39 39 33 37 40 33 32 36 40 35 35 40 39 31 32 40 32 38 34 33 36 36 33 33 37 38 34 37 36 36 32 38 31X3 30 24 20 25 23 22 22 23 26 28 23 30 28 25 23 26 28 30 27 23 22 30 27 23 23 26 29 27 25 23 28 26 29 25 22 27 27 29 21 27X2 20 14 12 15 14 12 12 13 16 20 17 20 18 15 13 16 18 20 17 19 13 19 20 14 16 18 16 20 18 11 15 20 19 16 14 14 14 13 12 11X1 10 4 2 6 5 2 2 3 6 10 5 4 1 9 8 1 9 2 1 3 9 10 8 2 1 7 5 8 9 1 5 10 9 6 4 4 4 3 2 1Data MatrixBi-cluster = Sub-Matrix with contiguous columns that enclosed a consumption patter12Bi-cluster of a Short-term Electrical Energy Consumption1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39Time SeriesElectric Energy Consumption SamplesX1X2X3X4X5b1b3b31 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40X5 46 42 45 43 44 50 42 41 44 48 42 50 48 45 43 46 48 50 47 49 46 46 46 41 47 46 43 49 43 41 45 50 49 46 44 44 44 43 42 41X4 31 31 37 38 39 40 37 39 39 33 37 40 33 32 36 40 35 35 40 39 31 32 40 32 38 34 33 36 36 33 33 37 38 34 37 36 36 32 38 31X3 30 24 20 25 23 22 22 23 26 28 23 30 28 25 23 26 28 30 27 23 22 30 27 23 23 26 29 27 25 23 28 26 29 25 22 27 27 29 21 27X2 20 14 12 15 14 12 12 13 16 20 17 20 18 15 13 16 18 20 17 19 13 19 20 14 16 18 16 20 18 11 15 20 19 16 14 14 14 13 12 11X1 10 4 2 6 5 2 2 3 6 10 5 4 1 9 8 1 9 2 1 3 9 10 8 2 1 7 5 8 9 1 5 10 9 6 4 4 4 3 2 1Data MatrixBi-cluster = Sub-Matrix with contiguous columns that enclosed a consumption patter13Parallel Evolutionary Bi-cluster14Parallel Search Covering – Master Slave ParadigmConsumption MatrixBi-Clusters15Parallel Search CoveringConsumption MatrixBi-Clusters16Parallel Search CoveringConsumption MatrixBi-Clusters17Parallel Search CoveringConsumption MatrixBi-Clusters18Parallel Search CoveringConsumption MatrizBi-Clusters19Worker nodeSequential Evolutionary Bi-clustering (SEBI)SEBI searches bi-clusters one-by-oneEBI calculates just one bi-cluster in each request.20F. Divina and J. S. Aguilar-Ruiz, "Biclustering of expression data with evolutionary computation," in IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 590-602, May 2006, doi: 10.1109/TKDE.2006.74.Worker nodeSequential Evolutionary Bi-clustering (SEBI)SEBI searches bi-clusters one-by-oneEBI calculates just one bi-cluster in each request.21Worker nodeSequential Evolutionary Bi-clustering (SEBI)SEBI searches bi-clusters one-by-oneEBI calculates just one bi-cluster in each request.22Worker nodeSequential Evolutionary Bi-clustering (SEBI)SEBI searches bi-clusters one-by-oneEBI calculates just one bi-cluster in each request.23Worker nodeSequential Evolutionary Bi-clustering (SEBI)SEBI searches bi-clusters one-by-oneEBI calculates just one bi-cluster in each request.24Simulations25Data Set➜ This dataset contains 744 samples of electric energy consumption for 184 sensors gathered in December of 2013.---------------744 simples--------------------------------184 sensors------26Performance vs Number of Workers/PCRelative performance of PSC over SCVE Row Var Volumefitness -0.72 0.07 -0.69VE -0.25 0.86Row Var -0.59• Fitness improves when VE and Volume improve.• Virtual error and Volume improves when K increases.27Some real bi-clusters calculatedThe patterns show abrupt changes of sensor groups at different times of the month. Such descriptions of electricity consumption are crucial to energy policy or decision-making. 28Conclusion and future works29Summary➜ We have developed a parallel evolutionary algorithm for a biclusteringproblem.➜ Biclustering helps to discover patterns of power consumption by describing which consumption points and time periods make up a pattern.➜ Quality measures, virtual error, row variance and volume, help to find coherent and highly variable patterns of power consumption.➜ We propose to extend to the multi-objective optimization approach➜ We want to consider other parallelization paradigms such as island parallelization.➜ We are interested in studying consumption patterns that are displaced in time.30¡Thank for your attention!31

Parallel evolutionary biclustering of short-term electric energy consumption

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Parallel evolutionary biclustering of short-term electric energy consumption

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