Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning
Abstract
This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Using a data set that encompasses electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set, particularly when comparing electricity usage during work hours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broader applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improving energy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability.
Downloads
References
L. D. Parker, “The COVID-19 office in transition : cost , efficiency and the social responsibility business case,” vol. 33, no. 8, pp. 1943–1967, 2020, doi: 10.1108/AAAJ-06-2020-4609.
E. Mantesi, K. Chmutina, and C. Goodier, “The office of the future: Operational energy consumption in the post-pandemic era,” Energy Res. Soc. Sci., vol. 87, no. June 2021, p. 102472, 2022, doi: 10.1016/j.erss.2021.102472.
S. H. Lin, H. Zhang, J. H. Li, C. Z. Ye, and J. C. Hsieh, “Evaluating smart office buildings from a sustainability perspective: A model of hybrid multi-attribute decision-making,” Technol. Soc., vol. 68, no. August 2021, p. 101824, 2022, doi: 10.1016/j.techsoc.2021.101824.
C. E. Danial, A. H. A. Mahmoud, and M. Y. Tawfik, “Methodology for retrofitting energy in existing office buildings using building information modelling programs,” Ain Shams Eng. J., vol. 14, no. 6, p. 102175, 2023, doi: 10.1016/j.asej.2023.102175.
Direktorat Jendral Energi Baru Terbarukan Dan Konservasi Energi (EBTKE), “Gedung Perkantoran,” Jakarta, 2019. [Online]. Available: https://simebtke.esdm.go.id/sinergi/sektor_pengguna_energi/detail/18/gedung-perkantoran
S. Ali, J. Zhang, A. Azeem, and A. Mahmood, “Impact Of Electricity Consumption On Economic Growth: An Application Of Vector Error Correction Model and Artificial Neural Networks,” J. Dev. Areas, vol. 54, no. 4, 2020, doi: 10.1353/jda.2020.0039.
O. Jogunola et al., “CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption,” Energies, vol. 15, no. 3, pp. 1–16, 2022, doi: 10.3390/en15030810.
D. Bunn and E. D. Farmer, Comparative models for electrical load forecasting. 1985. [Online]. Available: https://www.osti.gov/biblio/6256333
G. Chitalia, M. Pipattanasomporn, V. Garg, and S. Rahman, “Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks,” Appl. Energy, vol. 278, no. June, p. 115410, 2020, doi: 10.1016/j.apenergy.2020.115410.
S. Chen, Y. Ren, D. Friedrich, Z. Yu, and J. Yu, “Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates,” Energy AI, vol. 5, no. May, p. 100093, 2021, doi: 10.1016/j.egyai.2021.100093.
J. Hwang, D. Suh, and M. O. Otto, “Forecasting electricity consumption in commercial buildings using a machine learning approach,” Energies, vol. 13, no. 22, 2020, doi: 10.3390/en13225885.
C. Wang et al., “Impacts of climate change, population growth, and power sector decarbonization on urban building energy use,” Nat. Commun., vol. 14, no. 1, pp. 1–16, 2023, doi: 10.1038/s41467-023-41458-5.
G. Vijendar Reddy, L. J. Aitha, C. Poojitha, A. Naga Shreya, D. Krithika Reddy, and G. Sai Meghana, “Electricity Consumption Prediction Using Machine Learning,” in E3S Web of Conferences, 2023, pp. 1–9. doi: 10.1051/e3sconf/202339101048.
A. Floris, S. Porcu, R. Girau, and L. Atzori, “An iot-based smart building solution for indoor environment management and occupants prediction,” Energies, vol. 14, no. 10, pp. 1–17, 2021, doi: 10.3390/en14102959.
W. Alomoush, T. A. Khan, M. Nadeem, J. I. Janjua, A. Saeed, and A. Athar, “Residential Power Load Prediction in Smart Cities using Machine Learning Approaches,” 2022 Int. Conf. Bus. Anal. Technol. Secur. ICBATS 2022, no. February, 2022, doi: 10.1109/ICBATS54253.2022.9759024.
A. Barisic, V. Amaral, and M. Challenger, “Enhancing occupants comfort and well-being through a smart office setup,” 2020 43rd Int. Conv. Information, Commun. Electron. Technol. MIPRO 2020 - Proc., no. September, pp. 1825–1830, 2020, doi: 10.23919/MIPRO48935.2020.9245212.
A. Salosin, O. Gamayunova, and A. Mottaeva, “The effectiveness of the Smart Office system,” J. Phys. Conf. Ser., vol. 1614, no. 1, 2020, doi: 10.1088/1742-6596/1614/1/012028.
D. Ramos, P. Faria, A. Morais, and Z. Vale, “ScienceDirect Using decision tree to select forecasting algorithms in distinct electricity consumption context of an office building,” Energy Reports, vol. 8, pp. 417–422, 2022, doi: 10.1016/j.egyr.2022.01.046.
L. Peng, L. Wang, D. Xia, and Q. Gao, “Effective energy consumption forecasting using empirical wavelet transform and long short-term memory,” Energy, vol. 238, p. 121756, 2022, doi: 10.1016/j.energy.2021.121756.
M. K. M. Shapi, N. A. Ramli, and L. J. Awalin, “Energy consumption prediction by using machine learning for smart building: Case study in Malaysia,” Dev. Built Environ., vol. 5, no. December 2020, p. 100037, 2021, doi: 10.1016/j.dibe.2020.100037.
J. S. Chou, S. C. Hsu, N. T. Ngo, C. W. Lin, and C. C. Tsui, “Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners,” IEEE Syst. J., vol. 13, no. 3, pp. 3120–3128, 2019, doi: 10.1109/JSYST.2018.2890524.
B. I. S. Asif Karim, Rootul Patel, “Weather based Electricity Consumption Prediction using Big Data Analytics Model,” Int. J. Eng. Res. Technol., vol. 10, no. 4, pp. 158–163, 2021.
Y. Tian, J. Yu, and A. Zhao, “Predictive model of energy consumption for office building by using improved GWO-BP,” Energy Reports, vol. 6, pp. 620–627, 2020, doi: 10.1016/j.egyr.2020.03.003.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 3, pp. 1–21, 2021, doi: 10.1007/s42979-021-00592-x.
T. F. GODT, “Personal Electricity Consumption and Weather,” kaggle, 2021. https://www.kaggle.com/datasets/timongodt/energy-consumption-meteorological-features
DMI Bruger, “Danish Meteorological Institute - Open Data - Meteorological Observation Data - Data Information (metObs) - Parameters (metObs),” confluence.govcloud.dk, 2023. https://confluence.govcloud.dk/pages/viewpage.action?pageId=26476616&fbclid=IwAR3IIvZ6_gABMYjlUJ4lwSZX545XY0DEKGIBxVj13utg8Wmuky5w-9DUyS8
D. Mariano-Hernández, L. Hernández-Callejo, F. S. García, O. Duque-Perez, and A. L. Zorita-Lamadrid, “A review of energy consumption forecasting in smart buildings: Methods, input variables, forecasting horizon and metrics,” Appl. Sci., vol. 10, no. 23, pp. 1–27, 2020, doi: 10.3390/app10238323.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.
R. Rick and L. Berton, “Energy forecasting model based on CNN-LSTM-AE for many time series with unequal lengths,” Eng. Appl. Artif. Intell., vol. 113, no. March, p. 104998, 2022, doi: 10.1016/j.engappai.2022.104998.
A. M. Sharma, “Data Visualization,” in Data Science and Analytics, S. Kumari, K. K. Tripathy, and V. Kumbhar, Eds., Emerald Publishing Limited, 2020, pp. 1–22. doi: 10.1108/978-1-80043-876-720211002.
D. P. F. Möller, “Machine Learning and Deep Learning,” Adv. Inf. Secur., vol. 103, pp. 347–384, 2023, doi: 10.1007/978-3-031-26845-8_8.
P. Duboue, The Art of Feature Engineering: Essentials for Machine Learning. 2020. doi: 10.1017/9781108671682.
C. Fan, Y. Sun, Y. Zhao, M. Song, and J. Wang, “Deep learning-based feature engineering methods.pdf,” Appl. Energy, vol. 240, no. 1, pp. 35–45, 2019, doi: 10.1016/j.apenergy.2019.02.052.
Y. Bengio, Y. Lecun, and G. Hinton, “Deep learning for AI,” Commun. ACM, vol. 64, no. 7, pp. 58–65, 2021, doi: 10.1145/3448250.
H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent Neural Networks for Time Series Forecasting: Current status and future directions,” Int. J. Forecast., vol. 37, no. 1, pp. 388–427, 2021, doi: 10.1016/j.ijforecast.2020.06.008.
F. Petropoulos et al., “Forecasting: theory and practice,” Int. J. Forecast., vol. 38, no. 3, pp. 705–871, 2022, doi: 10.1016/j.ijforecast.2021.11.001.
S. Raschka, J. Patterson, and C. Nolet, “Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence,” Inf., vol. 11, no. 4, 2020, doi: 10.3390/info11040193.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
M. Guven and F. Uysal, “Time Series Forecasting Performance of the Novel Deep Learning Algorithms on Stack Overflow Website Data,” Appl. Sci., vol. 13, no. 8, 2023, doi: 10.3390/app13084781.
M. J. Jiménez-Navarro, M. Martínez-Ballesteros, F. Martínez-Álvarez, and G. Asencio-Cortés, “PHILNet: A novel efficient approach for time series forecasting using deep learning,” Inf. Sci. (Ny)., vol. 632, no. June 2022, pp. 815–832, 2023, doi: 10.1016/j.ins.2023.03.021.
S. Zaheer et al., “A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model,” Mathematics, vol. 11, no. 3, pp. 1–24, 2023, doi: 10.3390/math11030590.
C. Tang, L. Wang, Y. Wei, P. Wu, and H. Wei, “Time-Frequency Domain Variation Analysis and LSTM Forecasting of Regional Visibility in the China Region Based on GSOD Station Data,” Atmosphere (Basel)., vol. 14, no. 7, 2023, doi: 10.3390/atmos14071072.
B. C. Mateus, M. Mendes, J. T. Farinha, R. Assis, and A. M. Cardoso, “Comparing LSTM and GRU models to predict the condition of a pulp paper press,” Energies, vol. 14, no. 21, pp. 1–21, 2021, doi: 10.3390/en14216958.
M. Abumohsen, A. Y. Owda, and M. Owda, “Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms,” Energies, vol. 16, no. 5, pp. 1–31, 2023, doi: 10.3390/en16052283.
A. Zameer, F. Jaffar, F. Shahid, M. Muneeb, R. Khan, and R. Nasir, “Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU,” PLoS One, vol. 18, no. 10, p. e0285410, 2023, doi: 10.1371/journal.pone.0285410.
K. Zarzycki and M. Ławryńczuk, “Advanced predictive control for GRU and LSTM networks,” Inf. Sci. (Ny)., vol. 616, pp. 229–254, 2022, doi: 10.1016/j.ins.2022.10.078.
P. Dey, S. Dev, and B. S. Phelan, “BiLSTM−BiGRU: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, pp. 5166–5169. doi: 10.1109/IGARSS52108.2023.10282742.
P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, “Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach,” Fractal Fract., vol. 7, no. February 2023, pp. 1–18, 2023, doi: 10.3390/fractalfract7020203.
S. Ahuja, N. A. Shelke, and P. K. Singh, “A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India,” Signal, Image Video Process., vol. 16, pp. 579–586, 2022, doi: 10.1007/s11760-021-01988-1.
N. Putpuek, A. Putpuek, and A. Sungthong, “A Comparative Study of LSTM, GRU, BiLSTM and BiGRU to Predict Dissolved Oxygen,” in 2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2023, pp. 67–72. doi: 10.1109/JCSSE58229.2023.10202128.
U. Gupta, V. Bhattacharjee, and P. S. Bishnu, “StockNet—GRU based stock index prediction,” Expert Syst. Appl., vol. 207, no. June 2022, p. 117986, 2022, doi: 10.1016/j.eswa.2022.117986.
F. U. M. Ullah, A. Ullah, N. Khan, M. Y. Lee, S. Rho, and S. W. Baik, “Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU,” Complexity, vol. 2022, no. August 2022, pp. 1–15, 2022, doi: 10.1155/2022/2993184.
Y. Feng, Q. Duan, X. Chen, S. S. Yakkali, and J. Wang, “Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods,” Appl. Energy, vol. 291, no. December 2020, p. 116814, 2021, doi: 10.1016/j.apenergy.2021.116814.
D. Niu, M. Yu, L. Sun, T. Gao, and K. Wang, “Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism,” Appl. Energy, vol. 313, no. February, p. 118801, 2022, doi: 10.1016/j.apenergy.2022.118801.
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
