Abstract
With the continuous development of the global economy and the acceleration of urbanization, the annual energy consumption of buildings also occupies a considerable scale. In order to achieve energy saving and emission reduction in buildings, reasonable energy management for buildings is an important tool to achieve the goal of energy saving and emission reduction. In this paper, an improved Echo State Network method is used to predict building energy consumption. This improved echo state network can not only handle energy consumption data of a single building, but also combine multiple spatially correlated building energy consumption data to further improve the accuracy of energy prediction. The results show that the accuracy of the new model proposed in this paper for building energy consumption prediction is better than that of the classical ESN model and other classical machine learning models, and the model is well suited for end-to-end prediction tasks for multiple buildings. Combined with the clustering algorithm, it can also achieve acceleration for end-to-end prediction tasks.
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References
Allouhi, A., EI-Fouih, Y., Kousksou, T., Jamil, A., Zeraouli, Y., Mourad, Y.: Energy consumption and efficiency in buildings: current status and future trends. J. Clean. Prod. 109, 118–130 (2015)
Banihashemi, S., Ding, G., Wang, J.: Developing a hybrid model of prediction and classification algorithms for building energy consumption. Energy Procedia 110, 371–376 (2017)
Moga, L., Moga, I.: Building design influence on the energy performance. J. Appl. Eng. Sci. 5(1), 37–46 (2015)
Chang, C., Jing, Z., Zhu, N.: Energy saving effect prediction and post evaluation of air-conditioning system in public buildings. Energy Build. 43(11), 3243–3249 (2011)
Fong, W.K., Matsumoto, H., Lun, Y.F., et al.: System dynamic model for the prediction of urban energy consumption trends (2007)
Guo, Y., Wang, J., Chen, H., et al.: Machine learning-based thermal response time ahead energy demand prediction for building heating systems. Appl. Energy 221, 16–27 (2018)
Killian, M., Koze, M.: Ten questions concerning model predictive control for energy efficient building. Build. Environ. 105, 403–412 (2016)
Amasyali, K., EI-Gohary, N.M.: A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205 (2018)
Luo, X.J., Oyedele, L.O., Ajayi, A.O., et al.: Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renew. Sustain. Energy Rev. 131, 109980 (2020)
Ruiz, L.G.B., Rueda, R., Cuellar, M.P., et al.: Energy consumption forecasting based on elman neural networks with evolutive optimization. Expert Syst. Appl. 149(11), 57–68 (2017)
Lu, H., Cheng, F., et al.: Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: a case study of an intake tower. Energy 203, 117756 (2020)
Pham, A.-D., Ngo, N.-T., et al.: Prediction energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J. Clean. Prod. 260, 121082 (2020)
Liu, Y., Chen, H., et al.: Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: a case study in China. J. Clean. Prod. 272, 122542 (2020)
Liu, T., Tan, Z., Xu, C., et al.: Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. 208, 109675.1-109675.14 (2020)
Brandi, S., Piscitelli, M.S., Martellacci, M., et al.: Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings. Energy Build. 224, 110225 (2020)
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)
Sun, L., Jin, B., Yang, H., et al.: Unsupervised EEG feature extraction based on echo state network. Inf. Sci. 475, 1–17 (2019)
Lacy, S.E., Smith, S.L., Lones, M.A.: Using echo state networks for classification: a case study in parkinsons disease diagnosis. Artif. Intell. Med. 86, 53–59 (2018)
Chouikhi, N., Ammar, B., Rokbani, N., et al.: PSO-based analysis of echo state network parameters for time series forecasting. Appl. Soft Comput. 55, 211–225 (2017)
Sun, W., Liu, M.: Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Convers. Manag. 114, 197–208 (2016)
Qian, L., Zhou, W., Hz, C.: Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: a chain-structure echo state network approach. J. Clean. Prod. 261, 121151 (2020)
Jaeger, H., Lukosevicius, M., Popovici, D., et al.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335–352 (2007)
Miller, C., Meggers, F.: The building data genome project: an open, public data set from non-residential building electrical meters. Energy Procedia 122, 439–444 (2017)
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Sun, Y., Xu, J., Jiang, R., Wu, Z. (2021). An Improved Echo State Network Model for Spatial-Temporal Energy Consumption Prediction in Public Buildings. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_7
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DOI: https://doi.org/10.1007/978-981-16-5188-5_7
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