Abstract
This article presents a study to characterize the electricity consumption in residential buildings in Uruguay. Understanding residential electricity consumption is a relevant concept to identify factors that influence electricity usage, and allows developing specific and custom energy efficiency policies. The study focuses on two home appliances: air conditioner and water heater, which represents a large share of the electricity consumption of Uruguayan households. A data-analysis approach is applied to process several data sources and compute relevant indicators. Statistical methods are applied to study the relationships between different relevant variables, including appliance ownership, average income of households, and temperature, and the residential electricity consumption. A specific application of the data analysis is presented: a regression model to determine the consumption patterns of water heaters in households. Results show that the proposed approach is able to compute good values for precision, recall and F1-score and an excellent value for accuracy (0.92). These results are very promising for conducting an economic analysis that takes into account the investment cost of remotely controlling water heaters and the benefits derived from managing their demand.
This research was developed within a joint research project between Universidad de la república, the National Supercomputing Centar (Cluster-UY), and the national electricity company UTE.
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References
Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.: A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev. 124, 109792 (2020). https://doi.org/10.1016/j.rser.2020.109792
Alkhayat, G., Mehmood, R.: A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy AI 4, 100060 (2021). https://doi.org/10.1016/j.egyai.2021.100060
Amayri, M., Silva, C., Pombeiro, H., Ploix, S.: Flexibility characterization of residential electricity consumption: a machine learning approach. Sustain. Energy, Grids Netw. 32, 100801 (2022). https://doi.org/10.1016/j.segan.2022.100801
Chavat, J., Nesmachnow, S.: Analysis of residential electricity consumption by areas in Uruguay. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2020. CCIS, vol. 1359, pp. 42–57. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69136-3_4
Chavat, J., Nesmachnow, S., Graneri, J.: Non-intrusive energy disaggregation by detecting similarities in consumption patterns. Revista Facultad de Ingeniería Universidad de Antioquia (2020). https://doi.org/10.17533/udea.redin.20200370
Chavat, J., Nesmachnow, S., Graneri, J., Alvez, G.: ECD-UY, detailed household electricity consumption dataset of Uruguay. Scientific Data 9(1) (2022). https://doi.org/10.1038/s41597-022-01122-x
Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Sol. Energy 85(11), 2856–2870 (2011). https://doi.org/10.1016/j.solener.2011.08.027
Chupong, C., Plangklang, B.: Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement. Energy Procedia 9, 230–237 (2011). https://doi.org/10.1016/j.egypro.2011.09.024
Ding, M., Wang, L., Bi, R.: An ANN-based approach for forecasting the power output of photovoltaic system. Procedia Environ. Sci. 11, 1308–1315 (2011). https://doi.org/10.1016/j.proenv.2011.12.196
Energy Information Administration: international energy outlook (2021). https://www.eia.gov/outlooks/ieo/tables_side_xls.php. Accessed 3 July 2023. Washington, DC: U.S. EIA
Esteban, M., Fiori, I., Mujica, M., Nesmachnow, S.: Computational intelligence for characterization and disaggregation of residential electricity consumption. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2020. CCIS, vol. 1359, pp. 58–73. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69136-3_5
Fraccanabbia, N., Gomes, R., Molin, M.D., Rodrigues, S., dos Santos, L., Cocco, V.: Solar power forecasting based on ensemble learning methods. In: International Joint Conference on Neural Networks. IEEE (2020). https://doi.org/10.1109/ijcnn48605.2020.9206777
Iheanetu, K.: Solar photovoltaic power forecasting: a review. Sustainability 14(24), 17005 (2022). https://doi.org/10.3390/su142417005
Liu, H., Liang, J., Liu, Y., Wu, H.: A review of data-driven building energy prediction. Buildings 13(2), 532 (2023). https://doi.org/10.3390/buildings13020532
Massobrio, R., Nesmachnow, S.: Urban mobility data analysis for public transportation systems: a case study in Montevideo. Uruguay Appl. Sci. 10(16), 5400 (2020). https://doi.org/10.3390/app10165400
Muraña, J., et al.: Negotiation approach for the participation of datacenters and supercomputing facilities in smart electricity markets. Program. Comput. Softw. 46(8), 636–651 (2020). https://doi.org/10.1134/s0361768820080150
Muraña, J., Nesmachnow, S.: Simulation and evaluation of multicriteria planning heuristics for demand response in datacenters. SIMULATION 99(3), 003754972110200 (2021). https://doi.org/10.1177/00375497211020083
Nesmachnow, S., Iturriaga, S.: Cluster-UY: collaborative scientific high performance computing in Uruguay. In: Torres, M., Klapp, J. (eds.) ISUM 2019. CCIS, vol. 1151, pp. 188–202. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-38043-4_16
Pedro, H., Coimbra, C.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 86(7), 2017–2028 (2012). https://doi.org/10.1016/j.solener.2012.04.004
Porteiro, R., Chavat, J., Nesmachnow, S.: A thermal discomfort index for demand response control in residential water heaters. Appl. Sci. 11(21), 10048 (2021). https://doi.org/10.3390/app112110048
Porteiro, R., Chavat, J., Nesmachnow, S., Hernández-Callejo, L.: Demand response control in electric water heaters: evaluation of impact on thermal comfort. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2020. CCIS, vol. 1359, pp. 74–89. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69136-3_6
Porteiro, R., Hernández-Callejo, L., Nesmachnow, S.: Electricity demand forecasting in industrial and residential facilities using ensemble machine learning. Revista Facultad de Ingeniería Universidad de Antioquia 102, 9–25 (2020). https://doi.org/10.17533/udea.redin.20200584
Porteiro, R., Nesmachnow, S., Hernández-Callejo, L.: Short term load forecasting of industrial electricity using machine learning. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2019. CCIS, vol. 1152, pp. 146–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38889-8_12
Porteiro, R., Nesmachnow, S., Moreno-Bernal, P., Torres-Aguilar, C.E.: Computational intelligence for residential electricity consumption assessment: detecting air conditioner use in households. Sustain. Energy Technol. Assess. 58, 103319 (2023). https://doi.org/10.1016/j.seta.2023.103319
Theocharides, S., Alonso, R., Giacosa, G., Makrides, G., Theristis, M., Georghiou, G.: Intra-hour forecasting for a 50 MW photovoltaic system in Uruguay: baseline approach. In: IEEE 46\(^th\) Photovoltaic Specialists Conference. IEEE (2019). https://doi.org/10.1109/pvsc40753.2019.8980756
Zang, H., Cheng, L., Ding, T., Cheung, K.W., Wei, Z., Sun, G.: Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. Int. J. Electr. Power Energy Syst. 118, 105790 (2020). https://doi.org/10.1016/j.ijepes.2019.105790
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Llagueiro, P., Porteiro, R., Nesmachnow, S. (2024). Characterization of Household Electricity Consumption in Uruguay. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, vol 1938. Springer, Cham. https://doi.org/10.1007/978-3-031-52517-9_3
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