Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network

Abstract

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO2, noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches.

Full text

Citation: Mahjoub, S.; Labdai, S.;
Chrifi-Alaoui, L.; Marhic, B.;
Delahoche, L. Short-Term Occupancy
Forecasting for a Smart Home Using
Optimized Weight Updates Based on
GA and PSO Algorithms for an LSTM
Network. Energies 2023,16, 1641.
https://doi.org/10.3390/en16041641
Academic Editors: Paweł Piotrowski,
Grzegorz Dudek and Dariusz
Baczy´nski
Received: 24 December 2022
Revised: 21 January 2023
Accepted: 24 January 2023
Published: 7 February 2023
Copyright: © 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/).
energies
Article
Short-Term Occupancy Forecasting for a Smart Home Using
Optimized Weight Updates Based on GA and PSO Algorithms
for an LSTM Network
Sameh Mahjoub, Sami Labdai , Larbi Chrifi-Alaoui * , Bruno Marhic and Laurent Delahoche
Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne,
80000 Amiens, France
*Correspondence: larbi.alaoui@u-picardie.fr
Abstract:
In this work, we provide a smart home occupancy prediction technique based on environ-
mental variables such as CO
2
, noise, and relative temperature via our machine learning method and
forecasting strategy. The proposed algorithms enhance the energy management system through the
optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network
is a special deep learning strategy for processing time series prediction that has shown promising
prediction results in recent years. To improve the performance of the LSTM algorithm, particularly
for autocorrelation prediction, we will focus on optimizing weight updates using various approaches
such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the
proposed methods are evaluated using real available datasets. Test results reveal that the GA and the
PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks.
Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16%
and 99.97%, which proves the efficiency of the proposed approaches.
Keywords: deep neural networks; LSTM; time series prediction; optimisation; GA; PSO
1. Introduction
One of the most efficient systems to save energy is to reduce a building’s heating
and cooling load, which is mostly caused by heat transfer over its envelope. Smart build-
ings are required to provide permanent, healthy and comfortable indoor environments,
independent of exterior weather conditions [
1
,
2
]. Indeed, the major part of energy in
such buildings is used by Heating, Ventilation, and Air Conditioning (HVAC) systems,
which have a significant influence on both home comfort and the environment. Therefore,
managing these systems in residential structures should be tackled in order to increase
energy efficiency through improved energy planning [
3
]. One of the most essential features
of smart buildings is their ability to self-control the systems used to maintain the comfort
of the inside atmosphere while also minimizing energy use. Because HVAC systems are
the primary source of energy consumption in buildings, intelligent HVAC system control is
a current trend in research studies that necessitates the insertion of occupancy information
into the control process [
4
]. Moreover, the rise of smart buildings, as well as the pressing
need to reduce energy use, has rekindled interest in building energy demand prediction.
Intelligent controls are a solution for optimizing power consumption in buildings without
reducing interior comfort [
5
]. For example, in [
6
], a Model Predictive Control (MPC) is
developed to obtain a hybrid HVAC control with energy savings while maintaining of
thermal comfort. Building energy consumption prediction strives to achieve various goals
such as evaluating the impact of energy-saving interventions and assume energy demands
based on regular requirements. It can anticipate the fluctuations in power consumption of
certain events at specfic times that may modify the systems’ customary energy usage [
7
].
Furthermore, based on detailed and extensive studies, it was concluded that occupant
Energies 2023,16, 1641. https://doi.org/10.3390/en16041641 https://www.mdpi.com/journal/energies

Energies 2023,16, 1641 2 of 18
behavior is one of the most significant elements affecting residential structure energy use.
Occupancy behavior includes activities such as turning on and off lights, switching on and
off heating and cooling systems, and regulating the temperature.
Previous research has shown that various occupant demands and behaviors necessi-
tate specific technological solutions, which may induce or change behavior patterns, and
that occupant behavior affects the flexibility and deployment of technologies. However, the
lack of comprehensive knowledge of occupant behaviors in residential building leads to
misunderstanding and inaccurate decisions in both technical design and policy making [
8
].
The context of our research is energy efficiency. In recent years, energy efficiency has been
realized by improving the thermal performances of the building envelope’s insulation layer.
The research strategies aim to permanently adjust the comfort conditions to the living
situation, as well as to ensure greater energy supervision and management within the
smart buildings. To achieve this, it is important to automatically characterize the activities
of the building’s residents. The significant challenge in today’s new technical design for
smart buildings is understanding customer behaviors [
9
]. In the future, our occupancy pre-
diction approach will guarantee energy savings in a smart building environment. Ambient
intelligence is an important prerequisite for improving human quality of life.
The rest of this work is structured as follows: Section 2explains the technique em-
ployed in this project. Firstly, it offers the overall framework of the LSTM forecasting model.
Next, it presents, step-by-step, the implementation procedure of the suggested technique; it
includes descriptions of database processing, the parameters, and the assessment indicators.
Section 3features experimental details, as well as an analysis of the results. Finally, Section 4
provides some conclusions and future works.
2. Related Works
Building energy consumption is influenced by the thermal insulation, heating, ven-
tilation, air conditioning, lighting, and occupants’ behaviors [
10
]. Characterising human
activity has become an increasingly prominent application of machine learning in a many
disciplinary fields. Indeed, for the past two decades, researchers from several application
fields have investigated activity recognition by developing a variety of methodologies
and techniques for each of these key tasks. The prediction of human behaviour repre-
sents a key challenge, and many approaches have already been proposed in the industrial,
medical, home care, and energy efficiency domains, and many others [
11
]. For example,
in [
12
], an end-to-end technique for forecasting multi-zone interior temperatures using
LSTM-based sequence to sequence has been introduced. The goal of this prediction is to
improve the building’s energy efficiency while maintaining occupant comfort. Authors
in [
13
] also proposed implementing simple XGBoost machine learning methods to predict
the interior room temperature, relative humidity, and CO
2
concentration in a commercial
structure. The proposed technique presents a practical option because it does not require
a large data set for training. Additionally, these models eliminate the necessity for multiple
sensors, which create sophisticated and expensive networks. In [
14
], a short-term load
consumption forecasting approach for nonresidential buildings using artificial occupancy
attributes and based on Support Vector Machines (SVM) has been developed. However, the
determination of human behaviour in this work is imprecise. The authors in [
15
], present
a load forecasting model for office buildings based on artificial intelligence and regres-
sion analysis to effectively extract the cooling and heating load characteristics. However,
the model assumes that the building’s internal disturbing influences are steady. In [
16
],
an optimal deep learning LSTM model for forecasting electricity consumption utilizing
feature selection and a Genetic Algorithm (GA) is implemented. The goal of this suggested
technique is to determine the optimal time delays and number of layers for LSTM architec-
ture’s predictive performance optimisation, as well as to minimize overfitting, resulting
in more accurate and consistent forecasting. Furthermore, recently, machine learning ap-
proaches based on Artificial Neural Networks (ANNs) have been widely used to forecast the
thermal behavior of modern buildings for modeling HVAC systems.
As an example
, in [
17
],

Energies 2023,16, 1641 3 of 18
four comparative models have been developed and refined to forecast the inside tempera-
ture of a public building. These proposed techniques can be adapted to various scenarios.
However, we must keep in mind that the adoption of an online technique such as OMLP
(Online MultiLayer Perceptron) might be influenced by outliers. The authors also in [
18
]
tackle a non-linear autoregressive neural network methodology for forecasting interior
air temperature in the short and medium terms. Realistic artificial temperature data are
used to train the proposed model. The goal of this strategy is to make up for the lack of
real-world data collected by sensors in energy experiments. Thus, an improved technique
integrating real-time information and addressing possible noise or missing data is necessary
to prove the reliability of the proposed strategy in real scenarios. Differently from previous
research solutions, which typically rely on a basic and simple LSTM model, we designed
an optimised architecture exploiting GA and PSO algorithms to update weights and select
the optimal values that give the best prediction precision and reduce model overfitting.
As a matter of fact, these two methods (PSO and GA) were chosen due to their good
reputation in the literature, and they add a stochastic approach to the neural network that
resulted in better performance. We compared our results with the LSTM method, which
is considered the best neural approach in time series forecasting, as proven in previously
conducted works based on LSTM. As an example, Ref. [
19
] introduces comprehensive
comparative studies that include several deep learning methods used in forecasting extra-
short-term Plug-in Electric Vehicle (PEV) charging loads such as ANN, RNN, LSTM, gated
recurrent units (GRU), and bi-directional long short-term memory (Bi-LSTM). Among
these approaches, the LSTM model outperforms the others, and it is competent in giving
satisfactory results.
3. Materials and Methodologies
3.1. Data Description
A year of data were collected from a smart home between 1 January 2018 and
31 December 2018
with a resolution of 10 min. Each room of the house was equipped
with several sensors, including set points of the room temperature, CO
2
concentration,
pressure, noise, lighting, and occupancy:
• CO2values of a floor of house;
• Outdoor temperature;
• Noise values.
• Pressure values.
The concentration of these factors varied depending on the room; for example, the
concentration of CO
2
in the living room differed from that in the office or the kitchen.
Moreover, the CO
2
variable does not have a direct relationship with the interior temperature.
However, because CO
2
is a strong predictor of room occupancy, it may have a direct impact
on the indoor temperature during the cold season. The variation in the CO
2
, the noise, and
the temperature are given by Figures 1–3, respectively.
3.2. Data Pre-Processing
The prediction of building energy use based on an occupant behavior assessment is
a multivariate time series issue in which sensors create data that may contain uncertainty, re-
dundancy, missing values, non-unified time intervals, noise, and so on. Traditional machine
learning techniques struggle to reliably anticipate power usage due to unpredictable trend
components and seasonal trends. The collection of suitable data contributes to efficiently
addressing prediction challenges. As a result, several considerations should be made [20].
So, numerous techniques have been proposed to obtain meaningful inferences and insights;
nevertheless, these solutions are still in the early phases of development. Therefore, current
research is focusing on improving the procedures for processing and cleaning the collected
data in order to produce accurate prediction [21].

Energies 2023,16, 1641 4 of 18
0 1000 2000 3000 4000 5000 6000
Time (min)
400
600
800
1000
1200
1400
1600
CO2 concentration (ppm)
Figure 1. Overview of the CO2set points.
0 1000 2000 3000 4000 5000 6000
Time (min)
30
35
40
45
50
55
60
65
70
Noise (dB)
Figure 2. Overview of the room noise set points.
0 1000 2000 3000 4000 5000 6000
Time (min)
-10
0
10
20
30
Temperature (°C)
Figure 3. Overview of the room temperature set points.

Energies 2023,16, 1641 5 of 18
3.2.1. Missing Values
Many real-world datasets may include missing values for various reasons. So, training
a model using a dataset that has a large number of missing values can have a consider-
able influence on the machine learning model’s quality. To prevent information leakage,
missing data were interpolated using Exponential Moving Average (EMA). This method is
described in [22].
3.2.2. Normalisation
The data for a sequence prediction problem probably need to be normalised to the
range of [
−
1, +1] when training a neural network such as a long short-term memory
recurrent neural network. When a network is fit on unscaled data, it is possible for large
inputs to slow down the learning and convergence of that network and, in some cases,
prevent the network from effectively learning the problem. The Z-score is used for the
normalization, and the formula is given as [23]:
ZScore =x−xmean
xσ
(1)
where:
xσ=s1
n−1
n
∑
i=1
(xi−xmean )2(2)
xmean =1
n
n
∑
i=1
xi(3)
and nis the number of time periods.
4. Modeling Approaches
The main aim of this research is to investigate the performance of various occupancy
forecasting strategies to identify the most accurate ones. In fact, we choose three distinct
methods, based on a deep learning method: GA-LSTM and PSO-LSTM as optimiser based-
models and LSTM as a simple deep learning technique.
4.1. LSTM Architecture
Recurrent Neural Networks (RNNs) struggle with learning long-term dependencies.
LSTM-based models are an extension of RNNs that can solve the vanishing gradient prob-
lem and exploding gradient problem of RNNs and which perform more favorably than
RNN on longer sequences. LSTM models basically expand the memory of RNNs to allow
them to maintain and learn long-term input dependencies properly. This memory expan-
sion can recall data for a longer amount of time, allowing them to read, write, and delete
information from their memories. The LSTM memory is referred to as a “gate” structure
because it has the power to decide whether to keep or discard memory information [
24
,
25
].
A gate is a way of transferring information selectively that includes a sigmoid neural net-
work layer and a bitwise multiplication operations. The LSTM process and mathematical
representation consists mostly of the four phases listed below [26]:
1. Deciding to remove useless information:
ft=σ(wf[ht−1,Xt] + bf)(4)
where
ft
represents the forget gate and
σ
is the sigmoid activation function and it can be
defined as:
σ(x) = (1+e−x)−1(5)
This function is utilized for this gate to decide what information should be removed from
the LSTM’s memory. This decision is mainly dependent on the values of the previous
hidden layer output
ht−1
and the input
xt
. The output
ft
takes a value between 0 and 1,

Energies 2023,16, 1641 6 of 18
where 0 means fully discard the learned value and 1 means preserve the entire value.
wf
is
the recurrent weight matrix, while bfis the bias term.
2. Updating information:
it=σ(wi[ht−1,Xt] + bi)(6)
˜
ct=tanh(wc[ht−1,Xt] + bc)(7)
in which
it
is the input gate and denotes if the value needs to be updated or not and
˜
ct
designates a vector of new candidate values that will be added into the LSTM memory.
Indeed, the sigmoid layer determines which values require updating, and the
tanh
layer
generates a vector of new candidate values.
3. Updating the cell status:
ct=ft∗ct−1+it∗˜
ct(8)
where
ct
and
ct−1
represent the current and previous memory states, respectively. This
phase is carried out by updating the previous cell’s state, multiplying the old value by
ft
, deleting the information to be forgotten, and adding
it∗˜
ct
to generate a new candi-
date value.
4. Outputting information:
ot=σ(wo[ht−1,Xt] + bo)(9)
ht=ot∗tanh(ct)(10)
where
ot
is the output gate and
ht
is the current hidden layer outputs whose representations
are a value between
−
1 and 1. This step defines the ultimate result. To begin, a sigmoid
layer, represented by
ot
, selects which part of the cell state will be output. The cell state is
then processed by the
tanh
activation function and multiplied by the sigmoid layer output
to create the output.
A typical LSTM network is seen in Figure 4. LSTM layers are composed of memory
blocks rather than neurons. These memory blocks are interconnected across the layers,
and each block may contain one or more recurrently connected memory elements or cells.
As indicated in this figure (yellow shaded area), the flow of information is managed by
three types of gates: the forget gate ( ft), the input gate (It), and the output gate (Ot).
4.2. LSTM Model Settings and Optimisation
Optimizing an LSTM model entails establishing a set of model parameters that yields
the best model performance. The number of units and hidden layers and the optimiser,
activation function, batch size, and learning rate are typical examples of such elements. So,
the choice of a suitable algorithm is critical to success in addressing any type of optimisation
issue. Wolpert and Macready demonstrated this in their “no free lunch” theorem, which
states that no method is perfect for solving every type of optimisation issue. As a result,
the basic idea is to select an effective optimisation approach to solve a given hand-in
optimisation problem with less computational effort and a greater rate of convergence [
27
].
4.2.1. Genetic Algorithm (GA)
Genetic algorithms (GAs) have been around for over four decades. GAs are heuristic
search algorithms that provide answers to optimisation and search problems. The name
“GA” is derived from the biological terminology of natural selection, crossing, and muta-
tion. In reality, GAs simulate natural evolutionary processes [
28
]. Thus, a literature review
provides many instances of using GA in the analysis and optimisation of various elements
from many sectors, such as energy systems. Moreover, GA can be used for the optimisa-

Energies 2023,16, 1641 7 of 18
tion of ANN predictions or for the optimisation of ANN architecture [
29
]. GAs provide
a general and global optimisation process. Since the GA is a global search technique, it will
be less vulnerable to local search flaws such as back-propagation. The GA may be used to
design the network’s architecture as well as its weight. There have been various attempts
to utilise GAs to determine the architecture of a neural network and the link weights for
a fixed architecture network. Many attempts have been made to use a GA to determine the
architecture as well as the link weights.
*
*
*
+
σσ σ
tanh
tanh
Ct−1
ht−1
It
Ot
Cel l state ht
ht
Ct
ft~
Ct
Y
LSTM
LSTM
LSTM
x1
x2
xn
xt
Inputs Hidden layer
Output
Figure 4. A typical Long Short-Term Memory (LSTM) network topology.
4.2.2. Particle Swarm Optimization (PSO)
The particle swarm optimisation (PSO) method is a swarm-based stochastic optimi-
sation approach introduced by Eberhart and Kennedy (1995). This technique replicates
the social behavior of birds inside a flock to reach the food objective. A swarm of birds
approaches their food goal using a combination of personal and communal experience.
They constantly update their position based on their best position as well as the best
position of the entire swarm, and reunite themselves to form an ideal configuration [
30
].
This nature-inspired method is becoming increasingly popular due to its reliability and
easy implementation. In addition, classical neural networks do not operate well when
forecasting parameters within short intervals. Moreover, because of their dependability,
hybrid ANNs based on particle swarm optimisation have been frequently advocated in
literature reviews. The PSO method, like the GA, is used as an optimisation technique
within neural networks to optimise ANN forecasts or ANN architecture (the number of
layers, neurons, etc.) [31]. Thus, we use this algorithm to optimise the weights.
4.3. LSTM Network Parameters
The network’s trainable parameters, known as the trainable weights, influence the
network’s complexity. They are represented in LSTMs via connections between the input,
hidden, and output layers, as well as internal connections. The following formula is used
to calculate the Number of Trainable Weights (NTW) of a neural network with
x
inputs,
youtputs, and zLSTM cells in the hidden layer:
NTW =4xz +4zz +4z+yz +y(11)

Energies 2023,16, 1641 8 of 18
where:
- 4xz: the connection weights between the input layer and the hidden layer;
- 4zz: the hidden layer’s recursive weights;
- 4z: the hidden layer ’s bias;
-yz: the connection weights between the hidden layer and the output layer;
-y: the output layer ’s bias.
Choosing ideal neural network settings can frequently imply the difference between
mediocre and peak performance. However, there is limited information in the literature on
the selection of different neural network parameters
x
,
y
, and
z
; it requires the expertise
of professionals.
4.4. Train–Validation–Test dataset
The one-year target variables were divided into three datasets: the first served as
the training set, the second served as the test set, and depending on the length of the
output sequence, random samples drawn from the last part served as the validation
set. So, for the validation, we use cross-validation, which is a popular data resampling
approach for estimating the true forecasting prediction error of models and tuning model
parameters. This technique evaluates the generalization capabilities of prediction models
and prevents over-fitting. It is the process of generating numerous train–test splits from
the training data, which are then applied to adjust the model [
32
] .
k
-fold cross-validation
is identical to repeated random sub-sampling, but the sampling is performed in such
a manner that no two test sets overlap. The available learning set is divided into
k
disjoint
subsets of about equivalent size. Indeed, each time, one of the
k
subsets is utilised as the
validation/test batch, while the remaining (
k−
1) subsets are combined to form the training
set. The total efficacy of the model is calculated by averaging the error estimation over all
k
trials. Each sample is placed in a validation/test set precisely once and in the training
set (
k−
1) times [
33
]. Figure 5illustrates this process as a popular evaluation mechanism in
machine learning.
kk - 1
123 4
1st iteration
kk - 1
1 3 4
2nd iteration
kk - 1
124
3rd iteration
k- 1
123 4
kth iteration
1
2
3
k
Validation Training
Figure 5. k-fold cross-validation.
We train the LSTM with various architectures for 12-h forecasting of thermal parame-
ters such as CO
2
, noise, and temperature. As a result, the window size of the input and
output parameters is determined by the time scale of the chosen parameter prediction. We
apply the ADAM optimiser, which is one of the optimisation methods employed in deep
learning. The learning rate is fixed to 0.01 and gradually drops after every 50 epochs. We
train the LSTM with 60, 60, and 100 hidden units for the forecasting of the CO
2
, the noise,
and the temperature, respectively. The window size of the input and output parameters
depends on the time scale of the load prediction. The validation and training results of
each parameter are illustrated in Figures 6–8.

Energies 2023,16, 1641 9 of 18
01234567
Day
-1
-0.5
0
0.5
1
CO2
Observed
Validation
Figure 6. Training and validation of the CO2data.
01234567
Day
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
Noise
Observed
Validation
Figure 7. Training and validation of the noise data.
4.5. Evaluation Metrics
This study uses the Root Mean Square Error (
RMSE
) as the loss function and the
Mean Absolute Error (
MAE
) and the Correlation Coefficient (
CC
) to evaluate the various
performance measures. These indicators are measurements of the anticipated value’s
departure from the actual data, and they indicate the prediction’s overall inaccuracy. The
corresponding definition of each indicator is given by the following as [34]:
RMSE =v
u
u
t
1
N
N
∑
i=1
(yi−˜
yi)2(12)

Energies 2023,16, 1641 10 of 18
MAE =∑N
i=1|yi−˜
yi|
N(13)
CC =∑N
i=1(˜
yi−¯
p)(yi−¯
yi)
q∑N
i=1(˜
yi−¯
p)2∑N
i=1(yi−¯
yi)2
(14)
where
yi
and
˜
yi
represent the real value and the forecasted value at the time t,
N
denotes
the total time step, and ¯
yiand ¯
pare the average of the real value and the forecasted value,
respectively. The smaller the values of
RMSE
and
MAE
, the smaller the deviation of the
projected outcomes from the actual values. A value of
CC
closer to 1 indicates lower errors
and a more accurate prediction.
01234567
Day
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Temperature
Observed
Validation
Figure 8. Training and validation of the temperature data.
5. Experimental Results
5.1. Parameters Forecasting
We show in this research a forecast of the thermal characteristics of a smart house
outfitted with various types of sensors. The fundamental architecture of LSTM networks is
predetermined and immutable; each LSTM unit has a vector input of
n
values, including
the current value of the specified parameters (CO
2
, noise, and temperature) at time
t=
0 as
well as the past values. We create three neural networks with various designs, each one
adapted to the predicting parameter. After 10 min, these neural networks can forecast. We
can anticipate the full period of the required horizon by repeating the process and selecting
the appropriate parameters for these models.
5.2. CO2Forecasting
In the first experiment, we give the CO
2
prediction of a house for 12 h. Figures 9–11
show the predicted results obtained by the LSTM, the GA-LSTM, and the PSO-LSTM
algorithms, respectively. As shown, the predicted results are closer to the real data values
and the RMSE of each technique is quite low, which proves the forecasting performance of
the suggested strategies.

Energies 2023,16, 1641 11 of 18
Figure 9. CO2forecasting by LSTM.
Figure 10. CO2forecasting by GA-LSTM.

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Figure 11. CO2forecasting by PSO-LSTM.
5.3. Noise Forecasting
The second experiment also illustrates the noise prediction results for 12 h.
Figures 12–14 show the findings with the error rate of the LSTM, the GA-LSTM, and
the PSO-LSTM models. It appears that each model’s curve prediction retains the shape of
the real data curve.
Figure 12. Noise forecasting by LSTM.

Energies 2023,16, 1641 13 of 18
0 24 48
Time (hour)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
Noise
RMSE = 0.03
Observed
Forecasted
Figure 13. Noise forecasting by GA-LSTM.
0 24 48
Time (hour)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
Noise
RMSE = 0.026
Observed
Forecast
Figure 14. Noise forecasting by PSO-LSTM.
5.4. Temperature Forecasting
The third experiment shows the temperature forecasted results for 12 h. Figures 15–17
depict the results with the RMSE value of the LSTM, the GA-LSTM, and the PSO-LSTM
approaches. Likewise, each model’s curve prediction looks to keep the form of the real
data curve.

Energies 2023,16, 1641 14 of 18
0 24 48 72 96 120
Time (hour)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Temperature
RMSE = 0.028 Observed
Forecasted
Figure 15. Temperature forecasting by LSTM.
0 24 48 72 96 120
Time (hour)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
temperature
RMSE = 0.024
Observed
Forecasted
Figure 16. Temperature forecasting by GA-LSTM.

Energies 2023,16, 1641 15 of 18
0 24 48 72 96 120
Time (hour)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
temperature
RMSE = 0.007 Observed
Forecasted
Figure 17. Temperature forecasting by PSO-LSTM.
5.5. Analysis of Results
This work basically assesses the performance of the suggested model from two angles:
precision and running time. Tables 1–3provide the various performance measures for
testing predictions on the studied building.
We can see that the implemented approaches produce quite excellent results, and the
predicted findings are precise and dependable.
Tables 1–3reveal that the two performance metrics,
RMSE
and
MAE
, have small
values. These predictions are fairly close and representative to the real data. The correlation
coefficient (
CC
) is also very close to 1, which proves the high precision of the forecasting
strategies. As indicated in the tables and figures of forecasting results, the simple LSTM
model without optimisation gives the worst results compared with the GA-LSTM and the
PSO-LSTM techniques. We emphasize that the experimental results of the CO
2
prediction
show that the GA-LSTM outperforms the PSo-LSTM and the LSTM models with
RMSE
s of
0.0135, 0.0185, and 0.0281 and
CC
s of 99.80%, 99.62%, and 99.16% for GA-LSTM,
PSO-LSTM
,
and LSTM, respectively. For noise and temperature prediction, the performance of the PSO-
LSTM outperforms the GA-LSTM in terms of
RMSE
and
CC
. Overall, we have successfully
shown that the proposed optimisation techniques (GA-LSTM and PSO-LSTM networks)
may successfully extract relevant information from noisy human behavior data.
The statistical analysis of the obtained results shows that the proposed model tuned by
the two evolutionary metaheuristic search algorithms (GA and PSO) provides more precise
results than the benchmark LSTM model, whose parameters were established through
limited experience and a discounted number of experiments.
Table 1. Performance criteria of the CO2prediction.
Algorithms LSTM GA-LSTM PSO-LSTM
RMSE 0.0281 0.0135 0.0185
MAE 0.0102 0.0039 0.0061
CC 0.9916 0.9980 0.9962

Energies 2023,16, 1641 16 of 18
Table 2. Performance criteria of the noise prediction.
Algorithms LSTM GA-LSTM PSO-LSTM
RMSE 0.0405 0.0290 0.0256
MAE 0.0097 0.0070 0.0080
CC 0.9942 0.9970 0.9978
Table 3. Performance criteria of the temperature prediction.
Algorithms LSTM GA-LSTM PSO-LSTM
RMSE 0.0275 0.0243 0.0070
MAE 0.0063 0.0075 0.0017
CC 0.9968 0.9974 0.9997
6. Conclusions
In this work, we have proposed two optimised metaheuristic algorithms based on the
LSTM architecture for dealing with occupancy forecasting in the context of smart buildings.
The GA-LSTM and PSO-LSTM models give very satisfactory prediction results with a high
level of precision and reliability compared with the LSTM forecasting results. The choice of
these two methods (PSO and GA) is based on their reputation in literature. A comparison
shows that the implementation of the two metaheuristic algorithms (GA and PSO) for
the optimal configuration of occupancy forecasting derived an optimal LSTM model that
performs significantly better than the benchmark models, including other machine learning
approaches such as the basic LSTM model. The predicted values have been used to check
the presence of residents and then control real electrical consumption. This was carried
out to prove that the optimised LSTM can decrease power consumption, improve security,
and maintain comfort for the occupants. A potential field for future research would be
to perform thermal parameters forecasting, using recurrent neural networks, for various
construction such as hospitals, hotels, and public establishments. It would be worthwhile
to investigate whether a recurrent neural network can maintain such a high accuracy to
forecast thermal features and room occupancy rates in a smart building. Thus, future
studies will also focus on the deployment and integration of various hybrid optimisation
algorithms in recurrent neural networks such as the LSTM model in order to select the best
architecture, weights, and learning rate in order to achieve greater energy savings in the
building energy management system. As a result, our findings provide a solid foundation
for future research aimed at providing a more accurate assessment of building occupancy.
Nonetheless, the current findings will provide a basis for occupancy prediction, which
might be used to enhance our context-driven approaches for managing active building
systems such as the HVAC, lighting, and shading systems. Again, a forecasting model
for thermal characteristics and room occupancy rates with a low estimation error would
help energy producers in making operational, tactical, and strategic decisions. Finally,
better building load forecasting allows the implementation of the real-time management of
smart buildings.
Author Contributions:
Conceptualization, L.C.-A. and B.M.; methodology, S.M., B.M. and L.D.; soft-
ware, S.M. and S.L.; validation, S.M. and L.C.-A.; formal analysis, L.D. and L.C.-A.; investigation, S.M.
and S.L.; resources, B.M. and L.D.; data curation, B.M. and L.D.; writing—original draft preparation,
S.M.; writing—review and editing, L.C.-A. and S.L.; visualization, S.M.; supervision, L.C.-A. and
L.D.; project administration, B.M., L.D. and L.C.-A.; funding acquisition, B.M. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Energies 2023,16, 1641 17 of 18
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