Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Aji Prasetya Wibawa; Agung Bella Putra Utama; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Leonel Hernandez

doi:10.17977/um018v6i22023p170-187

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Aji Prasetya Wibawa, Agung Bella Putra Utama, Ade Kurnia Ganesh Akbari, Akhmad Fanny Fadhilla, Alfiansyah Putra Pertama Triono, Andien Khansa’a Iffat Paramarta, Faradini Usha Setyaputri, Leonel Hernandez

Abstract

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

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References

A. Sharif, S. Kocak, H. H. A. Khan, G. Uzuner, and S. Tiwari, “Demystifying the links between green technology innovation, economic growth, and environmental tax in ASEAN-6 countries: The dynamic role of green energy and green investment,” Gondwana Res., vol. 115, pp. 98–106, Mar. 2023.

P. Ma, S. Cui, M. Chen, S. Zhou, and K. Wang, “Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System,” Energies, vol. 16, no. 15, p. 5809, Aug. 2023.

L. Malka, F. Bidaj, A. Kuriqi, A. Jaku, R. Roçi, and A. Gebremedhin, “Energy system analysis with a focus on future energy demand projections: The case of Norway,” Energy, vol. 272, p. 127107, Jun. 2023.

S. Kapp, J.-K. Choi, and T. Hong, “Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters,” Renew. Sustain. Energy Rev., vol. 172, p. 113045, Feb. 2023.

Y. Zou, R. V. Donner, N. Marwan, J. F. Donges, and J. Kurths, “Complex network approaches to nonlinear time series analysis,” Phys. Rep., vol. 787, pp. 1–97, Jan. 2019.

A. Pranolo, Y. Mao, A. P. Wibawa, A. B. P. Utama, and F. A. Dwiyanto, “Robust LSTM With Tuned-PSO and Bifold-Attention Mechanism for Analyzing Multivariate Time-Series,” IEEE Access, vol. 10, pp. 78423–78434, 2022.

A. Pranolo, Y. Mao, A. P. Wibawa, A. B. P. Utama, and F. A. Dwiyanto, “Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction,” Knowl. Eng. Data Sci., vol. 5, no. 1, p. 53, Nov. 2022.

J. Naskath, G. Sivakamasundari, and A. A. S. Begum, “A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN,” Wirel. Pers. Commun., vol. 128, no. 4, pp. 2913–2936, 2023.

I. Koprinska, D. Wu, and Z. Wang, “Convolutional Neural Networks for Energy Time Series Forecasting,” in 2018 International Joint Conference on Neural Networks (IJCNN), Jul. 2018, pp. 1–8.

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, Jan. 2021.

G. Bathla, R. Rani, and H. Aggarwal, “Stocks of year 2020: prediction of high variations in stock prices using LSTM,” Multimed. Tools Appl., vol. 82, no. 7, pp. 9727–9743, Mar. 2023.

M. Yang and J. Wang, “Adaptability of Financial Time Series Prediction Based on BiLSTM,” Procedia Comput. Sci., vol. 199, pp. 18–25, 2022.

A. N. . F. Faisal, A. Rahman, M. T. M. Habib, A. H. Siddique, M. Hasan, and M. M. Khan, “Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh,” Results Eng., vol. 13, p. 100365, Mar. 2022.

A. R. F. Dewandra, A. P. Wibawa, U. Pujianto, A. B. P. Utama, and A. Nafalski, “Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN,” Int. J. Artif. Intell. Res., vol. 6, no. 1, 2022.

F. Kurniawan, S. Sulaiman, S. Konate, and M. A. A. Abdalla, “Deep learning approaches for MIMO time-series analysis,” Int. J. Adv. Intell. Informatics, vol. 9, no. 2, p. 286, Jul. 2023.

Y. Mao, A. Pranolo, A. P. Wibawa, A. B. Putra Utama, F. A. Dwiyanto, and S. Saifullah, “Selection of Precise Long Short Term Memory (LSTM) Hyperparameters based on Particle Swarm Optimization,” in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May 2022, pp. 1114–1121.

X. Zhou, A. Pranolo, and Y. Mao, “AB-LSTM: Attention Bidirectional Long Short-Term Memory for Multivariate Time-Series Forecasting,” in 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), Jun. 2023, pp. 1–6.

M. Elsaraiti, G. Ali, H. Musbah, A. Merabet, and T. Little, “Time Series Analysis of Electricity Consumption Forecasting Using ARIMA Model,” in 2021 IEEE Green Technologies Conference (GreenTech), Apr. 2021.

A. B. F. Khan, K. Kamalakannan, and N. S. S. Ahmed, “Integrating Machine Learning and Stochastic Pattern Analysis for the Forecasting of Time-Series Data,” SN Comput. Sci., vol. 4, no. 5, p. 484, Jun. 2023.

M. Skariah and C. D. Suriyakala, “Forecasting reservoir inflow combining Exponential smoothing, ARIMA, and LSTM models,” Arab. J. Geosci., vol. 15, no. 14, p. 1292, Jul. 2022.

A. P. Wibawa, A. B. P. Utama, H. Elmunsyah, U. Pujianto, F. A. Dwiyanto, and L. Hernandez, “Time-series analysis with smoothed Convolutional Neural Network,” J. Big Data, vol. 9, no. 1, p. 44, Dec. 2022.

V. Prema and K. U. Rao, “Development of statistical time series models for solar power prediction,” Renew. Energy, vol. 83, pp. 100–109, Nov. 2015.

S. Huber, H. Wiemer, D. Schneider, and S. Ihlenfeldt, “DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM model,” Procedia CIRP, vol. 79, pp. 403–408, 2019.

A. Tealab, H. Hefny, and A. Badr, “Forecasting of nonlinear time series using ANN,” Futur. Comput. Informatics J., vol. 2, no. 1, pp. 39–47, 2017.

K. APARNA, “Evolutionary computing based hybrid bisecting clustering algorithm for multidimensional data,” Sādhanā, vol. 44, no. 2, p. 45, Feb. 2019.

L. Vanneschi and S. Silva, “Particle Swarm Optimization,” in Natural Computing Series, 2023, pp. 105–111.

A. B. P. Utama, A. P. Wibawa, Muladi, and A. Nafalski, “PSO based Hyperparameter tuning of CNN Multivariate Time-Series Analysis,” J. Online Inform., vol. 7, no. 2, pp. 193–202, 2022.

M. Abo-Tabik, N. Costen, J. Darby, and Y. Benn, “Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events,” Sensors, vol. 20, no. 4, p. 1099, Feb. 2020.

W. J. Zhang, G. Yang, Y. Lin, C. Ji, and M. M. Gupta, “On Definition of Deep Learning,” in 2018 World Automation Congress (WAC), Jun. 2018, pp. 1–5.

D. A. Bashar, “Survey on Evolving Deep Learning Neural Network Architectures,” J. Artif. Intell. Capsul. Networks, vol. 2019, no. 2, pp. 73–82, Dec. 2019.

P. P. Shinde and S. Shah, “A Review of Machine Learning and Deep Learning Applications,” in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Aug. 2018, pp. 1–6.

H. Apaydin, H. Feizi, M. T. Sattari, M. S. Colak, S. Shamshirband, and K. W. Chau, “Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting,” Water (Switzerland), vol. 12, no. 5, pp. 1–18.

A. Zanfei, B. M. Brentan, A. Menapace, M. Righetti, and M. Herrera, “Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting,” Water Resour. Res., vol. 58, no. 7, Jul. 2022.

Z. Hu, J. Zhang, and Y. Ge, “Handling Vanishing Gradient Problem Using Artificial Derivative,” IEEE Access, vol. 9, pp. 22371–22377, 2021.

K. Smagulova and A. P. James, “A survey on LSTM memristive neural network architectures and applications,” Eur. Phys. J. Spec. Top., vol. 228, no. 10, pp. 2313–2324, Oct. 2019.

X. Meng, M. Liu, and Q. Wu, “Prediction of Rice Yield via Stacked LSTM,” Int. J. Agric. Environ. Inf. Syst., vol. 11, no. 1, pp. 86–95, Jan. 2020.

F. Shahid, A. Zameer, and M. Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,” Chaos, Solitons & Fractals, vol. 140, p. 110212, Nov. 2020.

H. Wang, Y. Zhang, J. Liang, and L. Liu, “DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction,” Neural Networks, vol. 157, pp. 240–256, Jan. 2023.

Q. Cheng, Y. Chen, Y. Xiao, H. Yin, and W. Liu, “A dual-stage attention-based Bi-LSTM network for multivariate time series prediction,” J. Supercomput., vol. 78, no. 14, pp. 16214–16235, Sep. 2022.

C. Hu, S. Martin, and R. Dingreville, “Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space,” Comput. Methods Appl. Mech. Eng., vol. 397, p. 115128, Jul. 2022.

X. Wang, N. Xie, and L. Yang, “A flexible grey Fourier model based on integral matching for forecasting seasonal PM2.5 time series,” Chaos, Solitons & Fractals, vol. 162, p. 112417, Sep. 2022.

W. Sun and C. Huang, “A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network,” Energy, vol. 207, p. 118294, Sep. 2020.

A. P. Wibawa, Z. N. Izdihar, A. B. P. Utama, L. Hernandez, and Haviluddin, “Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors,” in 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Apr. 2021, pp. 052–058.

A. P. Wibawa, “Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal,” J. Appl. Data Sci., vol. 4, no. 3, pp. 163–174, Sep. 2023.

Y. Yang, C. Yu, and R. Y. Zhong, “Generalized linear model-based data analytic approach for construction equipment management,” Adv. Eng. Informatics, vol. 55, p. 101884, Jan. 2023, doi: 10.1016/j.aei.2023.101884.

DOI: http://dx.doi.org/10.17977/um018v6i22023p170-187