Abstract
Smart grids are networks that distribute electricity by relying on advanced communication technologies, sensor measurements, and predictive methods, to quickly adapt the network behavior to different possible scenarios. In this context, the adoption of machine learning approaches to forecast the customer energy consumption is essential to optimize network planning operations, avoid unnecessary energy production, and minimize power shortages. However, classical forecasting methods are not able to take into account spatial and temporal autocorrelation phenomena, naturally introduced by the spatial proximity of consumers, and by the seasonality of the energy consumption trends.
In this paper, we investigate the adoption of several solutions to take into account spatio-temporal autocorrelation phenomena. Specifically, we investigate the contribution provided by the explicit representation of temporal information related to historical measurements using multiple strategies, as well as that of simultaneously predicting multiple future consumption measurements in a multi-step predictive setting. Finally, we investigate the effectiveness of injecting descriptive features to make the learning methods aware of the spatial closeness among the consumers.
The experimental evaluation performed on a real-world electrical network demonstrated the positive contribution of making the models aware of spatio-temporal autocorrelation phenomena, and proved the overall superiority of models based on the multi-step predictive setting.
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Notes
- 1.
Some rows in the normalized matrix can have a sum of 0, when the corresponding consumer has no other consumers falling in its neighborhood, according to maxDist.
References
Anselin, L.: Local indicators of spatial association – LISA. Geogr. Anal. 27(2), 93–115 (1995)
Berriel, R.F., Lopes, A.T., Rodrigues, A., Varejao, F.M., Oliveira-Santos, T.: Monthly energy consumption forecast: a deep learning approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4283–4290. IEEE (2017)
Bontempi, G., Ben Taieb, S.: Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int. J. Forecast 27(3), 689–699 (2011)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and control, 5th edn. Wiley (2015)
Ceci, M., Corizzo, R., Fumarola, F., Malerba, D., Rashkovska, A.: Predictive modeling of PV energy production: how to set up the learning task for a better prediction? IEEE Trans. Ind. Inf. 13(3), 956–966 (2016)
Cabral, D.A., Legey, J., et al.: Electricity consumption forecasting in Brazil: a spatial econometrics approach. Energy 126, 124–131 (2017)
Dray, S., Legendre, P., Peres-Neto, P.R.: Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196(3–4), 483–493 (2006)
Gower, J.C.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3–4), 325–338 (1966)
Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95–104 (2018)
Masum, S., Liu, Y., Chiverton, J.: Multi-step time series forecasting of electric load using machine learning models. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10841, pp. 148–159. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91253-0_15
Mignone, P., Pio, G.: Positive unlabeled link prediction via transfer learning for gene network reconstruction. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., Raś, Z.W. (eds.) ISMIS 2018. LNCS (LNAI), vol. 11177, pp. 13–23. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01851-1_2
Ohtsuka, Y., Oga, T., Kakamu, K.: Forecasting electricity demand in Japan: a Bayesian spatial autoregressive ARMA approach. Comput. Stat. Data Anal. 54(11), 2721–2735 (2010)
Serafino, F., Pio, G., Ceci, M.: Ensemble learning for multi-type classification in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 30(12), 2326–2339 (2018)
Shih, S.-Y., Sun, F.-K., Lee, H.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8), 1421–1441 (2019). https://doi.org/10.1007/s10994-019-05815-0
Stojanova, D., Ceci, M., Appice, A., Džeroski, S.: Network regression with predictive clustering trees. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 333–348. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23808-6_22
Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067–7083 (2012)
Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Econ. Geogr. 46(sup1), 234–240 (1970)
Williams, K.T., Gomez, J.D.: Predicting future monthly residential energy consumption using building characteristics and climate data: a statistical learning approach. Energy Build. 128, 1–11 (2016)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: AAAI 2020, vol. 34 , no. 01, pp. 1234–1241 (2020)
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D’Aversa, A., Polimena, S., Pio, G., Ceci, M. (2022). Leveraging Spatio-Temporal Autocorrelation to Improve the Forecasting of the Energy Consumption in Smart Grids. In: Pascal, P., Ienco, D. (eds) Discovery Science. DS 2022. Lecture Notes in Computer Science(), vol 13601. Springer, Cham. https://doi.org/10.1007/978-3-031-18840-4_11
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