# A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}, air pollution, and global warming are all caused by the usage of energy produced from fossil fuels. According to studies, buildings account for 39 percent of the overall consumption of energy and 38 percent of global ${\mathrm{CO}}_{2\text{}}$ emissions in the world. The major cause for the rise in energy utilization is the developmentof urbanization in recent decades [1].

- To present a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN).
- To preprocess energy consumption dataset using linear-scaling normalization.
- To optimize the model using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA).

## 2. Literature Review

## 3. Proposed Work

**(a)****Historical Energy Consumption Database**

^{2}and 13 reading rooms. A full schedule of opening and closing times of the reading rooms was supplied which was considered as the occupancy measure. There is a nearby weather station that collects daily dry-bulb temperatures. Cooling, heating, lighting, ventilation, and plug loads were all taken into account when calculating the building’s energy usage. From 9 October 2009 to 15 January 2010, a total of 2472 time-step data were gathered at hourly intervals [11]

**(b)****Data Preprocessing Using Linear Scaling Normalization (LSN)**

_{max}and

_{min}are the maximum and minimum values of the features, correspondingly. The preprocessed data is categorized into training and testing datasets. Overall, 70% of the preprocessed data are considered as a training set, and the remaining data are noted as a testing set.

**(c)****Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN)**

_{1}, v

_{2}, …, VP), and features of the input data are extracted using ‘n’ hidden layers (h

_{1}, h

_{2}, …, HQ).

_{k}

^{R}, a

_{k}

^{L}, and a

_{k}

^{M}denote the right bound, the left bound, and the center of connection weights, respectively. ${\tilde{b}}_{l}$, and ${\tilde{w}}_{k,}$ can also be obtained by similar methods.

^{L}(α) and G

^{R}(α) are the left and right boundaries of an interval [G

^{L}(α), G

^{R}(α)], which express the α-cut of the fuzzy number $\tilde{G}\left(\alpha \right)$V.

_{cd}is the value of the assigned weight. ${\delta}_{c}$ is the neuron’s activation state at a time ‘m’ according to Equation (10).

**(d)****Adaptive Fuzzy Adam Optimization Algorithm (AFAOA)**

_{1}and β

_{2}represent the exponential decay rates, and z denotes the time-step. f($\theta $) denotes the stochastic objective function, ${\theta}_{0}$ and ${\theta}_{z}$ represent the initial and final parameter vectors, respectively, h

_{z}and v

_{z}denote first and second-moment vectors, respectively, ${\widehat{\theta}}_{Hz}$ and ${\widehat{v}}_{z}$ are bias-corrected moment estimates, and ${g}_{z}^{2}$ means the element-wise square.

## 4. Results and Discussion

_{O}is the actual value and T

_{p}is the determined value of energy consumed, n represents the sample size and i = 1 to n.

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Zhong, H.; Wang, J.; Jia, H.; Mu, Y.; Lv, S. Vector field-based support vector regression for building energy consumption prediction. Appl. Energy
**2019**, 242, 403–414. [Google Scholar] [CrossRef] - Fayaz, M.; Kim, D. A prediction methodology of energy consumption based on a deep extreme learning machine and comparative analysis in residential buildings. Electronics
**2018**, 7, 222. [Google Scholar] [CrossRef] - Wang, R.; Lu, S.; Li, Q. A multi-criteria comprehensive study on a predictive algorithm of hourly heating energy consumption for residential buildings. Sustain. Cities Soc.
**2019**, 49, 101623. [Google Scholar] [CrossRef] - Bourdeau, M.; Qiang Zhai, X.; Nefzaoui, E.; Guo, X.; Chatellier, P. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustain. Cities Soc.
**2019**, 48, 101533. [Google Scholar] [CrossRef] - Kim, T.Y.; Cho, S.B. Predicting residential energy consumption using CNN-LSTM neural networks. Energy
**2019**, 182, 72–81. [Google Scholar] [CrossRef] - Ruiz, L.G.B.; Rueda, R.; Cuéllar, M.P.; Pegalajar, M.C. Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Syst. Appl.
**2018**, 92, 380–389. [Google Scholar] [CrossRef] - Touzani, S.; Granderson, J.; Fernandes, S. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Build.
**2018**, 158, 1533–1543. [Google Scholar] [CrossRef] - Shen, Y.; Wei, R.; Xu, L. Energy consumption prediction of a greenhouse and optimization of daily average temperature. Energies
**2018**, 11, 65. [Google Scholar] [CrossRef] - Wang, J.Q.; Du, Y.; Wang, J. LSTM-based long-term energy consumption prediction with periodicity. Energy
**2020**, 197, 117197. [Google Scholar] [CrossRef] - Shao, M.; Wang, X.; Bu, Z.; Chen, X.; Wang, Y. Prediction of energy consumption in hotel buildings via support vector machines. Sustain. Cities Soc.
**2020**, 57, 102128. [Google Scholar] [CrossRef] - Li, K.; Xie, X.; Xue, W.; Dai, X.; Chen, X.; Yang, X. A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction. Energy Build.
**2018**, 174, 323–334. [Google Scholar] [CrossRef] - Ribeiro, M.; Grolinger, K.; ElYamany, H.F.; Higashino, W.A.; Capretz, M.A. Transfer learning with seasonal and trend adjustment for cross-building energy forecasting. Energy Build.
**2018**, 165, 352–363. [Google Scholar] [CrossRef] - Wang, Z.; Wang, Y.; Zeng, R.; Srinivasan, R.S.; Ahrentzen, S. Random Forest-based hourly building energy prediction. Energy Build.
**2018**, 171, 11–25. [Google Scholar] [CrossRef] - Smarra, F.; Jain, A.; De Rubeis, T.; Ambrosini, D.; D’Innocenzo, A.; Mangharam, R. Data-driven model predictive control using random forests for building energy optimization and climate control. Appl. Energy
**2018**, 226, 1252–1272. [Google Scholar] [CrossRef] - Rahman, A.; Srikumar, V.; Smith, A.D. Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl. Energy
**2018**, 212, 372–385. [Google Scholar] [CrossRef] - Fan, C.; Sun, Y.; Zhao, Y.; Song, M.; Wang, J. Deep learning-based feature engineering methods for improved building energy prediction. Appl. Energy
**2019**, 240, 35–45. [Google Scholar] [CrossRef] - Yang, Y.; Wang, J.; Wang, B. Prediction model of the energy market by long short-term memory with random system and complexity evaluation. Appl. Soft Comput.
**2020**, 95, 106579. [Google Scholar] [CrossRef] - Muralitharan, K.; Sakthivel, R.; Vishnuvarthan, R. Neural network-based optimization approach for energy demand prediction in smart grid. Neurocomputing
**2018**, 273, 199–208. [Google Scholar] [CrossRef] - Wei, Y.; Xia, L.; Pan, S.; Wu, J.; Zhang, X.; Han, M.; Zhang, W.; Xie, J.; Li, Q. Prediction of occupancy level and energy consumption in an office building using blind system identification and neural networks. Appl. Energy
**2019**, 240, 276–294. [Google Scholar] [CrossRef] - Zhang, X.; Zhang, J.; Zhang, J.; Zhang, Y. Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network. Geofluids
**2021**, 2021, 7271383. [Google Scholar] [CrossRef] - Yan, K.; Li, W.; Ji, Z.; Qi, M.; Du, Y. A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access
**2019**, 7, 157633–157642. [Google Scholar] [CrossRef] - Reynolds, J.; Rezgui, Y.; Kwan, A.; Piriou, S. A zone-level, building energy optimization combining an artificial neural network, a genetic algorithm, and model predictive control. Energy
**2018**, 151, 729–739. [Google Scholar] [CrossRef] - Gassar, A.A.A.; Yun, G.Y.; Kim, S. A data-driven approach to the prediction of residential energy consumption at urban scales in London. Energy
**2019**, 187, 115973. [Google Scholar] [CrossRef] - Ullah, I.; Ahmad, R.; Kim, D. A prediction mechanism of energy consumption in residential buildings using a hidden Markov model. Energies
**2018**, 11, 358. [Google Scholar] [CrossRef] - Ahmad, T.; Chen, H. Potential of three variant machine-learning models for forecasting district-level medium-term and long-term energy demand in a smart grid environment. Energy
**2018**, 160, 1008–1020. [Google Scholar] [CrossRef] - Işık, E.; Inallı, M. Artificial neural networks and adaptive neuro-fuzzy inference systems approach to forecasting the meteorological data for HVAC: The case of cities for Turkey. Energy
**2018**, 154, 7–16. [Google Scholar] [CrossRef] - Işık, E.; İnallı, M.; Celik, E. ANN and ANFIS approach to calculate the heating and cooling degree day values: The case of provinces in Turkey. Arab. J. Sci. Eng.
**2019**, 44, 7581–7597. [Google Scholar] [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sorguli, S.; Rjoub, H. A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network. *Energies* **2023**, *16*, 2844.
https://doi.org/10.3390/en16062844

**AMA Style**

Sorguli S, Rjoub H. A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network. *Energies*. 2023; 16(6):2844.
https://doi.org/10.3390/en16062844

**Chicago/Turabian Style**

Sorguli, Sarhang, and Husam Rjoub. 2023. "A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network" *Energies* 16, no. 6: 2844.
https://doi.org/10.3390/en16062844