A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
DOI: http://dx.doi.org/10.30630/joiv.7.4.01865
Abstract
A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level
Keywords
Full Text:
PDFReferences
N. A. Jasim, H. TH, and S. A. L. Rikabi, “Design and Implementation of Smart City Applications Based on the Internet of Things.,” Int. J. Interact. Mob. Technol., vol. 15, no. 13, 2021.
S. Je and J. Huh, “Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platform,” Int. J. Commun. Syst., vol. 34, no. 2, p. e4056, 2021.
S. Tiwari et al., “Machine learning‐based model for prediction of power consumption in smart grid‐smart way towards smart city,” Expert Syst., vol. 39, no. 5, p. e12832, 2022.
Z. Wang, M. Ogbodo, H. Huang, C. Qiu, M. Hisada, and A. Ben Abdallah, “AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in smart grid platform,” IEEE Access, vol. 8, pp. 226409–226421, 2020.
M. Zekić-Sušac, S. Mitrović, and A. Has, “Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities,” Int. J. Inf. Manage., vol. 58, p. 102074, 2021.
S. Mahjoub, L. Chrifi-Alaoui, B. Marhic, and L. Delahoche, “Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks,” Sensors, vol. 22, no. 11, p. 4062, 2022.
M. Humayun, M. S. Alsaqer, and N. Jhanjhi, “Energy optimization for smart cities using iot,” Appl. Artif. Intell., vol. 36, no. 1, p. 2037255, 2022.
S. Mahjoub, S. Labdai, L. Chrifi-Alaoui, B. Marhic, and L. Delahoche, “Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network,” Energies, vol. 16, no. 4, p. 1641, 2023.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
K. Chen, “APSO-LSTM: an improved LSTM neural network model based on APSO algorithm,” in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 12151.
M. Sajjad et al., “A novel CNN-GRU-based hybrid approach for short-term residential load forecasting,” IEEE Access, vol. 8, pp. 143759–143768, 2020.
M. Khalil, A. S. McGough, Z. Pourmirza, M. Pazhoohesh, and S. Walker, “Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review,” Eng. Appl. Artif. Intell., vol. 115, p. 105287, 2022.
M. G. M. Almihat, M. T. E. Kahn, K. Aboalez, and A. M. Almaktoof, “Energy and Sustainable Development in Smart Cities: An Overview,” Smart Cities, vol. 5, no. 4, pp. 1389–1408, 2022.
Y. Kaluarachchi, “Implementing data-driven smart city applications for future cities,” Smart Cities, vol. 5, no. 2, pp. 455–474, 2022.
K. Amarasinghe, D. L. Marino, and M. Manic, “Deep neural networks for energy load forecasting,” in 2017 IEEE 26th international symposium on industrial electronics (ISIE), IEEE, 2017, pp. 1483–1488.
K. Yan, X. Wang, Y. Du, N. Jin, H. Huang, and H. Zhou, “Multi-step short-term power consumption forecasting with a hybrid deep learning strategy,” Energies, vol. 11, no. 11, p. 3089, 2018.
X. Wang, T. Zhao, H. Liu, and R. He, “Power consumption predicting and anomaly detection based on long short-term memory neural network,” in 2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA), IEEE, 2019, pp. 487–491.
N. Al Khafaf, M. Jalili, and P. Sokolowski, “Application of deep learning long short-term memory in energy demand forecasting,” in International conference on engineering applications of neural networks, Springer, 2019, pp. 31–42.
J. Q. Wang, Y. Du, and J. Wang, “LSTM based long-term energy consumption prediction with periodicity,” energy, vol. 197, p. 117197, 2020.
Z. A. Khan, A. Ullah, W. Ullah, S. Rho, M. Lee, and S. W. Baik, “Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy,” Appl. Sci., vol. 10, no. 23, p. 8634, 2020.
Z. Lin, L. Cheng, and G. Huang, “Electricity consumption prediction based on LSTM with attention mechanism,” IEEJ Trans. Electr. Electron. Eng., vol. 15, no. 4, pp. 556–562, 2020.
N. Somu, G. R. MR, and K. Ramamritham, “A deep learning framework for building energy consumption forecast,” Renew. Sustain. Energy Rev., vol. 137, p. 110591, 2021.
R. Markovic, E. Azar, M. K. Annaqeeb, J. Frisch, and C. van Treeck, “Day-ahead prediction of plug-in loads using a long short-term memory neural network,” Energy Build., vol. 234, p. 110667, 2021.
C. Zhou, Z. Fang, X. Xu, X. Zhang, Y. Ding, and X. Jiang, “Using long short-term memory networks to predict energy consumption of air-conditioning systems,” Sustain. Cities Soc., vol. 55, p. 102000, 2020.
B. Nettasinghe, S. Chatterjee, R. Tipireddy, and M. M. Halappanavar, “Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains,” in International Conference on Machine Learning, PMLR, 2023, pp. 25890–25903.
M. Bilgili, N. Arslan, A. ŞEKERTEKİN, and A. YAŞAR, “Application of long short-term memory (LSTM) neural network based on deeplearning for electricity energy consumption forecasting,” Turkish J. Electr. Eng. Comput. Sci., vol. 30, no. 1, pp. 140–157, 2022.
R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” in International conference on machine learning, PMLR, 2015, pp. 2342–2350.
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.
H. Ikhlasse, D. Benjamin, C. Vincent, and M. Hicham, “Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders,” Alexandria Eng. J., vol. 61, no. 12, pp. 11565–11577, 2022.
X. Li, X. Ma, F. Xiao, C. Xiao, F. Wang, and S. Zhang, “Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA),” J. Pet. Sci. Eng., vol. 208, p. 109309, 2022.
S. Jung, J. Moon, S. Park, and E. Hwang, “An attention-based multilayer GRU model for multistep-ahead short-term load forecasting,” Sensors, vol. 21, no. 5, p. 1639, 2021.
S. Agarwal, “Data mining: Data mining concepts and techniques,” in 2013 international conference on machine intelligence and research advancement, IEEE, 2013, pp. 203–207.
F. Soldan, A. Maldarella, G. Paludetto, E. Bionda, F. Belloni, and S. Grillo, “Characterization of electric consumers through an automated clustering pipeline,” in 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), IEEE, 2022, pp. 1–5.
R. Chiosa, M. S. Piscitelli, C. Fan, and A. Capozzoli, “Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries,” Energy Build., vol. 270, p. 112302, 2022.
Q. Yuan, Y. Pi, L. Kou, F. Zhang, Y. Li, and Z. Zhang, “Multi-source data processing and fusion method for power distribution internet of things based on edge intelligence,” Front. Energy Res., vol. 10, p. 891867, 2022.
I. Ullah, K. Liu, T. Yamamoto, R. E. Al Mamlook, and A. Jamal, “A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability,” Energy Environ., vol. 33, no. 8, pp. 1583–1612, 2022.
I. Atik, “A New CNN-Based Method for Short-Term Forecasting of Electrical Energy Consumption in the Covid-19 Period: The Case of Turkey,” IEEE Access, vol. 10, pp. 22586–22598, 2022.
Y. Li, Z. Zhu, D. Kong, H. Han, and Y. Zhao, “EA-LSTM: Evolutionary attention-based LSTM for time series prediction,” Knowledge-Based Syst., vol. 181, p. 104785, 2019.
F. Sommer and M. Stuke, “An efficient and fast method to calculate integral experimental correlation coefficients–S2Cor,” Ann. Nucl. Energy, vol. 157, p. 108209, 2021.