A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

Aoqi Xu, Man-Wen Tian, Behnam Firouzi, Khalid A. Alattas, Ardashir Mohammadzadeh and Ebrahim Ghaderpour ()
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Aoqi Xu: School of Economics, Fujian Normal University, Fuzhou 350007, China
Man-Wen Tian: National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China
Behnam Firouzi: Vibrations and Acoustics Laboratory (VAL), Mechanical Engineering Department, Ozyegin University, Istanbul 34794, Turkey
Khalid A. Alattas: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Ardashir Mohammadzadeh: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China
Ebrahim Ghaderpour: Department of Earth Sciences, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185 Rome, Italy

Sustainability, 2022, vol. 14, issue 16, 1-12

Abstract: A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.

Keywords: restricted Boltzmann machine; mid-term load forecasting; machine learning; artificial intelligence; contrastive divergence algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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