Abstract
This paper presents how to manage the power consumption history in a microgrid, clusters days according to their time series patterns, and develops a prediction model for next day demand. Daily consumption patterns, each of which consists of quarter-hourly records, are grouped into 6 clusters, taking advantage of the dynamic time warping method in measuring the similarity between all feasible pairs of days. We select 3 main parameters for the cluster prediction of the next day, namely, month, day-of-week, and day-high temperature given by the weather forecast. For machine learning, learning patterns are generated after joining tables of power consumption, weather archive and day-group association on a daily basis. The next step builds an artificial neural network model using well-known open software. The model shows the accuracy of 67%, making it possible to estimate next day behavior, select the best demand model, and estimate power demand for vehicle-to-grid trades.
This research was supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute. (Grant number: R15XA03-62).
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References
Ramchrun, S., Vytelingum, R., Rogers, A., Jennings, N.: Putting the ‘Smarts’ into the smart grid: a grand challenge for artificial intelligence. Commun. ACM 55(4), 89–97 (2012)
Bayram, I., Shakir, M., Abdallah, M., Qaraqe, K.: A survey on energy trading in smart grid. In: IEEE Global Conference on Signal and Information Processing, pp. 258–262 (2014)
Lee, J., Park, G.: A heuristic-based electricity trade coordination for microgrid-level V2G services. Int. J. Veh. Des. 69(1/2/3/4), 208–223 (2015)
Lee, J., Park, G., Cho, Y., Kim, S., Jung, J.: Spatio-temporal analysis of state-of-charge streams for electric vehicles. In: 4th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 368–369 (2015)
Goiri, I., Le, K., Nguyen, T., Guitart, J., Torres, J., Bianchini, R.: GreenHadoop: leveraging green energy in data-processing frameworks. In: Proceedings of Eurosys (2012)
Aman, S., Simmhan, Y., Prasanna, V.: Holistic measures for evaluating prediction models in smart grids. IEEE Trans. Knowl. Data Eng. 27(2), 475–488 (2015)
Duerden, C., Shark, L.-K., Hall, G., Howe, J.: Prediction of granular time-series energy consumption for manufacturing jobs from analysis and learning of historical data. In: Annual Conference on Information Science and Systems, pp. 625–630 (2016)
Brunsdon, C., Comber, L.: An Introduction to R for Spatial Analysis & Mapping. SAGE Publication Ltd, Thousand Oaks (2015)
Nissen, S.: Neural Network Made Simple. Software 2.0 (2005)
Karnouskos, S., Ilic, D., Silva, G.: Assessment of an enterprise energy service platform in a smart grid city pilot. In: IEEE International Conference on Industrial Informatics, pp. 24–29 (2013)
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Lee, J., Park, GL. (2017). V2G Demand Prediction Based on Daily Pattern Clustering and Artificial Neural Networks. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_14
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DOI: https://doi.org/10.1007/978-3-319-59072-1_14
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