# 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

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**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