Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Abstract

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Full text

Research Article
Machine Learning Algorithms for Predicting Energy
Consumption in Educational Buildings
Khaoula Elhabyb ,
1
Amine Baina,
1
Mostafa Bellafkih ,
1
and Ahmed Farouk Deifalla
2
1
National Institute of Posts and Telecommunications (INPT), Rabat, Morocco
2
Future University Cairo in Egypt, Cairo, Egypt
Correspondence should be addressed to Ahmed Farouk Deifalla; ahmed.deifalla@fue.edu.eg
Received 14 December 2023; Revised 19 March 2024; Accepted 26 March 2024; Published 13 May 2024
Academic Editor: Saleh N. Al-Saadi
Copyright © 2024 Khaoula Elhabyb et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency
in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by
analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and
evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and
gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of
these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error
(RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of
the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each
building’s energy consumption.
1. Introduction
Artificial intelligence is rapidly being integrated into various
industries, such as healthcare, finance, and smart grids.
Among these human-centric applications, the use of AI in
smart buildings has attracted significant attention from a
large community [1]. Smart buildings, which have been a
subject of research since the 1980s, utilize advanced technol-
ogy, data analytics, and automation systems to optimize
operations, enhance occupant comfort and productivity,
and reduce costs and energy consumption [2]. These build-
ings incorporate sensors, devices, and control systems to
monitor lighting, HVAC systems, security, and access con-
trols. Real-time data on occupancy, temperature, air quality,
and energy use can be analyzed to identify optimization
opportunities. The primary aim is to create an efficient, com-
fortable, and sustainable environment for residents while
reducing costs and ecological impact.
The smart building industry is experiencing significant
growth as society becomes more connected and digital.
According to statistics from MarketsandMarkets [3], the
industry is projected to expand at a compound annual
growth rate (CAGR) of 10.5% between 2020 and 2025,
reaching a value of $108.9 billion. This growth is driven
by factors such as increased energy usage and expenses,
advancements in machine learning and the Internet of
Things (IoT), the push for net zero energy buildings, and
regulatory changes that encourage the adoption of smart
building systems and services. Figure 1 presents the fore-
casted global market side from 2020 to 2030. Expanding
on the findings of the Zion Marketing research study [4],
it reveals the market value of 40,760 million in 2016, with
projections of a substantial growth trajectory to 61,900
million by 2024, with a CAGR exceeding 34%. This indi-
cates a rapid expansion within the market, indicating
robust trends and significant economic development dur-
ing the study period.
The AI sector being discussed is experiencing signifi-
cant growth due to the integration of the Internet of
Things (IoT) and machine learning (ML). IoT sensors col-
lect data about buildings and occupants, such as tempera-
ture, humidity, occupancy, and electricity consumption.
Hindawi
International Journal of Energy Research
Volume 2024, Article ID 6812425, 19 pages
https://doi.org/10.1155/2024/6812425

This data is centralized for optimizing building operations,
improving resident comfort, and reducing energy usage.
ML, on the other hand, is a powerful tool for processing
large amounts of data from various sources. It analyzes
this data to identify patterns and predict future events,
such as equipment failures, enabling preventative mainte-
nance [5].
The American Council for Energy-Efficient Economy
(ACEEE) [6] suggests that commercial buildings can signif-
icantly reduce their energy bills by up to 30% by imple-
menting energy-efficient technologies such as smart
thermostats and controlled lighting. The US Department of
Energy [7] reports that commercial buildings account for a
significant portion of total energy consumption and green-
house gas emissions in the US. This highlights the impor-
tance of buildings that can predict energy consumption
and plan efficiently to reduce energy usage. Intel research
[8] indicates also that energy consumption prediction has
the potential to achieve operational cost savings, staffpro-
ductivity gains, and energy usage reductions. Given these
findings, the primary emphasis will be on forecasting the
energy usage of smart buildings, with a specific focus on
educational facilities, which will be analyzed for the first
time. Understanding and predicting energy consumption
in educational environments are paramount for optimizing
resource allocation, implementing effective efficiency mea-
sures, and establishing sustainable and cost-effective opera-
tional procedures [9]. By focusing on this sector, valuable
insights can be gained to inform strategies for enhancing
energy efficiency and sustainability in educational buildings,
ultimately contributing to improved resource management
and environmental conservation efforts.
The research concentrates on energy management
within smart buildings, aiming to forecast power consump-
tion through three distinct approaches: a traditional statisti-
cal approach employing the random forest algorithm, a deep
learning approach utilizing long short-term memory
(LSTM), and a hybrid approach leveraging the gradient
boosting regressor algorithm. These three techniques were
chosen to investigate a research gap regarding to the major-
ity of data-driven methodologies. While significant progress
has been made in this area, limited attention has been given
to utilizing streaming and temporal data for forecasting
buildings’energy demand. This gap will be addressed
through the utilization of real historical electricity data. The
used data is analyzed to evaluate model performance and
accuracy, aiming to identify the most effective approach for
smart building energy management. The research is aimed
at optimizing forecasting techniques through rigorous
comparative analysis, leveraging the strengths of LSTM, RF,
and GBR models. The study highlights the importance of
advanced machine learning in shaping smart building strate-
gies and is aimed at enhancing sustainability and efficiency in
energy usage. Insights from this research will inform future
advancements in energy management practices for sustain-
able development. The article is structured into several delin-
eated sections, each serving a specific purpose:
(i) Introduction: This section introduces the applica-
tion of AI within the smart building sector, setting
the context for the study
(ii) Literature analysis: Here, a comparative examina-
tion of various ML algorithms used for energy pre-
diction in smart building systems is provided,
drawing insights from existing research
(iii) Methodology: This section outlines the systematic
approach adopted in the study, encompassing data
analytics, model development, and model evalua-
tion processes
(iv) Results and discussions: Findings obtained from the
methodology are presented, followed by a compara-
tive analysis that juxtaposes these results with prior
research initiatives
(v) Conclusion: This section synthesizes the results and
provides conclusions, offering perspectives on the
implications of the study’sfindings for the field of
smart building energy management
2. Literature Review
A recent study conducted by the International Energy
Agency [10] has revealed concerning levels of energy
CAGR 10.6%
Revenue (USD Mn/Bn)
$121.3 billion
$67.4 billion
2019 2020 2021 2022 2023 2024 2025
Ye a r
2026 2027 2028 2029 2030
Figure 1: The global smart building market size [3].
2 International Journal of Energy Research

consumption in buildings. The study found that buildings
are responsible for a significant portion of electricity con-
sumption and overall energy consumption in urban areas.
Buildings account for 72% of total electricity consumption
and 38% of average energy consumption in urban areas.
Additionally, buildings contribute to almost 40% of total
carbon dioxide pollution in urban areas. A smart building
is a modern infrastructure that incorporates automated
control systems and uses data to improve the building’s
performance and occupants’comfort. Figure 2 presents
the smart building functionalities and its most important
axis of work.
The top technology companies are currently prioritizing
IoT (Internet of Things) and AI (artificial intelligence). The
future of building innovation is expected to focus on achiev-
ing maximum energy efficiency, and this challenge can be
addressed by integrating AI-powered systems like machine
learning (ML) and deep learning. ML systems continuously
improve themselves, leading to advancements in various AI
research areas [12]. ML involves algorithms that allow them
to respond to inputs from their environment and identify
nonlinear connections in complicated or uncertain systems.
ML is divided into four major categories based on the type of
learning task they manage: supervised learning, unsuper-
vised learning, semisupervised learning, and reinforcement
learning.
(i) Supervised learning is a method of developing a
machine learning model by using a labeled data
set. In this process, each data point in the set is asso-
ciated with a known intended output. The model is
trained to predict the output
(ii) Unsupervised learning: in contrast to traditional
supervised learning, developing a model on an unla-
beled data set involves working with data where the
target outputs are unknown. In this scenario, the
model is not explicitly instructed on what to search
for but instead learns
(iii) Semisupervised learning is a learning approach that
combines supervised and unsupervised learning. In
this approach, the model is trained using a data set
that is partly labeled, meaning that some of the data
points have known labels
(iv) Reinforcement learning where a model is trained to
make a series of decisions in a changing environ-
ment. The model learns through trial and error,
receiving feedback in the form of rewards or costs
Energy consumption prediction is a valuable technique
that involves forecasting the amount of energy a system or
device will use within a specific time frame. This technique
serves various purposes, such as optimizing energy usage,
predicting future energy demands, and identifying potential
inefficiencies in energy consumption. To predict energy
consumption, different methods can be employed, including
statistical models, machine learning algorithms, and physics-
based models. The choice of technique depends on factors
such as data availability, system complexity, and the desired
level of accuracy. In this particular case, the focus is on
utilizing machine learning algorithms to predict energy
consumption by leveraging historical data and other relevant
factors.
The quality and relevance of the data used in machine
learning algorithms greatly influence their performance. In
a study conducted by Ahajjam et al. [13] on Moroccan
Buildings’Electricity Consumption Data Set, electricity con-
sumption was categorized into three types: whole premises
(WP), individual loads (IL), and circuit-level (CL) data.
(1) Labeled WP: Labeled whole premises (WP) con-
sumption data refers to electricity usage data col-
lected from 13 households in the MORED data set.
Energy
Smart meters,
demand response Wat er
Smart meters, use
and ow sensing
HVAC
Fans, variable air
volume, air quality
Elevators
Maintenance,
performance
Access and security
Badge in, cameras,
integration perimeter,
doors
Lighting
Occupancy sensing
Fire
Functionality checks,
detector service
24/7 monitoring
Condition monitoring,
parking lot utilization
PEHV charging
Charging of hybrid
and electric vehicles
Figure 2: The global smart building market size [11].
3International Journal of Energy Research

This data is valuable as it includes not only the raw
electricity consumption measurements but also addi-
tional information that can assist in analyzing,
modeling, and comprehending the patterns of elec-
tricity usage in different households
(2) Labeled IL: Ground-truth electricity consumption
refers to the electricity consumption data of individ-
ual loads (IL) that have been labeled or annotated
with accurate information. This involves recording
and labeling the operational states of specific loads,
such as refrigerators or air conditioners when they
are turned on or offat specific times. Having this
ground-truth information is valuable for researchers
and analysts as it allows for accurate load disaggrega-
tion, energy management, and appliance recognition
(3) CL: Measurements in the context of energy refer to
the circuit-level energy measurements obtained from
the electrical mains of a premises. These measure-
ments provide information about the overall energy
consumption of a circuit and can be used to under-
stand the energy consumption of a group of loads
The current work focuses on three educational buildings
located at Down Town University. Further information
about these buildings will be provided next. The subsequent
section presents a literature review on energy consumption
forecasting in various buildings using multiple machine
learning algorithms.
2.1. Traditional Machine Learning Approach. ML algorithms
have been utilized to tackle the primary challenges of
physics-driven methods in load prediction. For instance,
Somu et al. [14] developed eDemand, a new building energy
use forecasting model, using long short-term memory net-
works and an improved sine cosine optimization algorithm,
and as a result, the model outperformed previous state-of-
the-art models in real-time energy load prediction. Next,
Suranata et al. [15] focused on predicting energy consump-
tion in kitchens. They used a feature engineering technique
and a short-term memory (LSTM) model. Principal compo-
nent analysis (PCA) was applied to extract important fea-
tures, and the LSTM model was used on two tables. In
addition, Shapi et al. [16] developed a prediction model for
energy demand making use of the Microsoft Azure cloud-
based machine learning framework, The methodology of
the prediction model is provided using three distinct tech-
niques, including support vector machine, artificial neural
network, and k-nearest neighbors. The study focuses on
real-world applications in Malaysia, with two tenants from
an industrial structure chosen as case studies. The experi-
mental findings show that each tenant’s energy consumption
has a particular distribution pattern, and the suggested
model can accurately estimate energy consumption for each
renter. To forecast daily energy consumption based on
weather data, Faiq et al. [17] developed a new energy usage
prediction technique for institutional buildings using long
short-term memory (LSTM). The model, trained using
Malaysian Meteorological Department weather forecasting
data, outperformed support vector regression (SVR) and
Gaussian process regression (GPR) with the best RMSE
scores. The dropout method reduces overfitting, and Shap-
ley’s additive explanation is used for feature analysis. Accu-
rate energy consumption estimates can help detect and
diagnose system faults in buildings, aiding in energy policy
implementation. Further, Kawahara et al. [18] explore the
application of various machine learning models to predict
voltage in lithium-ion batteries. The study includes algo-
rithms such as support vector regression, Gaussian process
regression, and multilayer perceptron. The hyperparameters
of each model were optimized using 5-fold cross-validation
on training data. The data set used consists of both simula-
tion data, generated by combining driving patterns and
applying an electrochemical model, and experimental data.
The performance of the ML models was evaluated using both
simulation and experimental data, with different data sets
created to simulate variations in state of charge distribution.
2.2. Deep Learning and Hybrid Approaches. Additionally,
various networks integrate multiple techniques to devise
data-driven approaches. These integrated mechanisms are
commonly referred to as hybrid networks. For example,
Mohammed et al. [19] focus on the application of an
intelligent control algorithm in HVAC systems to enhance
energy efficiency and thermal comfort. The authors pro-
pose integrating SCADA systems with an intelligent build-
ing management system to optimize heat transmission
coefficients and air temperature values. Genetic algorithms
are employed to maintain user comfort while minimizing
energy consumption. Similar to [19], Aurna et al. [20]
compare the performance of ARIMA and Holt-Winters
models in predicting energy consumption data in Ohio
and Kentucky. The study finds that the Holt-Winters model
is more accurate and effective for long-term forecasting. The
authors recommend further research to consider other
parameters, and environmental factors, and explore hybrid
models for better short-term load forecasting. Next, Fer-
doush et al. [21] developed a hybrid forecasting model for
time series electrical load data. The model combines random
forest and bidirectional long short-term memory methods
and was tested on a 36-month Bangladeshi electricity con-
sumption data set. The results showed that the hybrid model
outperformed standard models in terms of accuracy. The
study emphasizes the effectiveness of the hybrid machine
learning approach in improving short-term load forecasting
accuracy in the dynamic electric industry. In their study,
He and Tsang [22] developed a hybrid network combining
long short-term memory (LSTM) and improved complete
ensemble empirical mode decomposition with adaptive noise
(iCEEMDAN) to optimize electricity consumption. They
divided the initial power consumption data into patterns
using iCEEMDAN and used Bayesian-optimized LSTM to
forecast each mode independently. In the same direction,
Jin et al. [23] proposed an attention-based encoder-decoder
network with Bayesian optimization for short-term electrical
load forecasting, using a gated recurrent unit recurrent neu-
ral network for time series data modeling and a temporal
attention layer for improved prediction accuracy and
4 International Journal of Energy Research

precision. Further in their study, Olu-Ajayi et al. [24] used
various machine learning techniques to predict yearly build-
ing energy consumption using a large data set of residential
buildings. The model allows designers to enter key building
design features and anticipate energy usage early in the
development process. DNN was found to be the most effi-
cient predictive model, motivating building designers to
make informed choices and optimize structures. Jang et al.
[25] created three LSTM models to compare the effects of
incorporating operation pattern data on prediction perfor-
mance. The model using operation pattern data performed
the best, with a CVRMSE of 17.6% and an MBE of 0.6%.
The article by Ndife et al. [26] presents a smart power
consumption forecast model for low-powered devices. The
model utilizes advanced methodologies, such as the
ConvLSTM encoder-decoder algorithm, to accurately pre-
dict power consumption trends. The performance evalua-
tion of the model demonstrates improved accuracy and
computational efficiency compared to traditional methods.
Also, Duong and Nam [27] developed a machine learning
system that monitors electrical appliances to improve elec-
tricity usage behavior and reduce environmental impact.
The system utilizes load and activity sensors to track energy
consumption and operating status. After three weeks of test-
ing, the system achieved a state prediction accuracy of
93.60%. In their approach, Vennila et al. [28] propose a
hybrid model that integrates machine learning and statistical
techniques to improve the accuracy of predicting solar
energy production. The model also helps in reducing place-
ment costs by emphasizing the significance of feature selec-
tion in forecasting. In the sale context, Kapp et al. [29]
developed a supervised machine learning model to address
energy use reduction in the industrial sector. They collected
data from 45 manufacturing sites through energy audits
and used various characteristics and parameters to predict
weather dependency and production reliance. The results
showed that a linear regressor over a transformed feature
space was a better predictor than a support vector machine.
In their research, Bhol et al. [30] propose a new method for
predicting reactive power based on real power demand. They
utilize a flower pollination algorithm to optimize their model
and show that it outperforms other models like GA, PSO, and
FPA. Asiri et al. [31] used an advanced deep learning model
for accurate load forecasting in smart grid systems. They use
hybrid techniques, including LSTM and CNN, feature engi-
neering, and wavelet transforms, to enhance forecasting
accuracy and efficiency. The results show significant
improvements in short-term load prediction, outperforming
traditional forecasting methods.
Table 1 contains detailed information about the algo-
rithms used, performance evaluation measurements, and
the advantages and disadvantages of each approach.
3. Methodology
This research predicts power usage in three buildings of a
private research university using a data set collected from
January 2020 to January 2023. The university is known
for its excellence in education and research across various
disciplines. The buildings under study (referred to as CLAS,
NHAI, and Cronkite) are all part of the same institution
and serve distinct functions. Building CLAS, an abbrevia-
tion of Center of Law and Society, mainly consists of an
amphitheater and offices, and building NHAI, which
means Nursing and Health Innovation, consists of offices
and laboratories. In contrast, Cronkite consists of class-
rooms and seminar halls.
The buildings are equipped with IoT sensors connected
to power intel sockets, and the collected data is sorted on
an open-source website server [32]. The prediction method
will use three machine learning algorithms: long short-
term memory (LSTM), random forest (RF), and gradient
boosting regressor (GBR). The data will be analyzed and
prepared before being used to train and test the models.
The methodology for forecasting energy consumption
will be divided into three sections:
(1) Data analysis involves evaluating raw data to under-
stand patterns and characteristics of electrical power
consumption data.
(2) Model training trains machine learning models,
using past data to identify patterns and correlations
between input characteristics and day power use
(3) Model test models evaluation using validation met-
rics to assess their performance and accuracy.
3.1. Data Analysis
3.1.1. Data Preparation. This study focuses on the process of
data preparation in machine learning, which is time-
consuming and computationally challenging due to the pres-
ence of missing values and uneven value scales between
features. The data was prepared using two techniques: impu-
tation of missing data and standardization. The imputation
procedure was carried out using the probabilistic principal
component analysis (PPCA) approach, a maximum likeli-
hood estimate-based technique that estimates missing values
using the expectation-maximization (EM) algorithm. This
method is developed from the principal component analysis
(PCA) method, which is used for data compression or
dimensionality reduction. The resulting cleaned data was
then subjected to standardization, also known as Z-score
normalization, to ensure an even distribution of the data
above and below the mean value as shown in equation (3):
xstandardized =x−μ
σ, 1
where μrepresents the mean, σdenotes the standard devia-
tion, and xis the original data points.
3.1.2. Data Normality Analysis. This research conducted a
normality test on each renter’s data set to determine its distri-
bution. This test is crucial for model construction and is espe-
cially important for larger sample sizes. Understanding the
data set distribution can provide valuable insights into the
prediction outcome. Kurtosis measures distribution peaks,
5International Journal of Energy Research

Table 1: Previous research in ML-driven building energy use prediction.
Authors Algorithm Data set Performance evaluation Pros Cons
Somu et al.
[14]
(i) ARIMA
(ii) Genetic algorithm-LSTM
(iii) Sine cosine optimization
algorithm-LSTM
(i) The KReSIT power consumption
data set is sourced from the Indian
Institute of Technology (IIT) in
Mumbai, India.
(i) ARIMA: MAE: 0.3479;
MAPE: 21.3333; MSE: 0.1661;
RMSE: 0.4076.
(ii) Genetic algorithm-LSTM:
MAE: 0.1804; MAPE: 5.9745;
MSE: 0.0432; RMSE: 0.2073.
(iii) (ISCOA-LSTM): MAE: 0.0819;
MAPE: 4.9688; MSE: 0.0135;
RMSE: 0.1164.
(i) Improved forecasting accuracy
(ii) Improved forecasting accuracy
(iii) Real-world applicability
(i) Sensitivity to initialization
(ii) Convergence speed
Suranata
et al. [15] (i) Long short-term memory (i) NL (i) RMSE = 62 013;MAE = 26 982;
MAPE = 12 876
(i) The ability to effectively predict
energy consumption patterns in
time series data.
(i) Time-consuming training
Ferdoush
et al. [21]
(i) LSTM
(ii) RF-bi-LSTM hybrid model
(iii) Bidirectional long short-term
memory (LSTM)
(i) Bangladesh Power Development
Board covered 36 months.
(i) LSTM: MSE = 0 4776;
RMSE = 0 691;MAE = 0 5578;
MAPE = 148 7.
(ii) Bi-LSTM: MSE = 0 2943;
RMSE = 0 5425;MAE = 0 4317;
MAPE = 194 80.
(iii) RF-bi-LSTM: MSE = 0 1673;
RMSE = 0 4090;MAE = 0 3070
MAPE = 193 49.
(i) Stable learning characteristics.
(ii) Moderate generalization gap in
learning loss analysis.
(i) The hybrid model may require
specific data to utilize the
strengths of random forest and
bidirectional LSTM effectively.
Yaqing et al.
[22]
(i) EMD-BO-LSTM
(ii) iCEEMDAN
(i) Real power consumption data of a
university campus for 12 months
(i) EMD-BO-LSTM: MAE = 155 77;
RMSE = 203 4;MAPE = 10 41%;
R2 = 0 8478.
(ii) iCEEMDAN-BO-LSTM:
MAE = 40 841;RMSE = 59 68;
MAPE = 2 5563%;R2 = 0 986.
(i) Adaptability and efficiency
(ii) Enhanced prediction accuracy (i) NL
Ndife et al.
[26] (i) ConvLSTM encoder-decoder
(i) Two million measurements were
gathered over 47 months from a
residential location in Sceaux,
France.
(i) RMSE on the model: 358 kWh
RMSE on the persistence model:
465 kWh RMSE on model A:
530 kWh RMSE on model B:
450.5 kwh
(i) Improved forecast accuracy
(ii) Suitable for low-powered devices
(iii) Efficient training and prediction
time
(i) Model complexity
Duong et al.
[27] (i) Multiple layer perceptron
(i) 215 data points on the power
consumption and on/offstatus of
electrical devices, in Vietnam.
(i) RMSE: 10.468
(ii) MAPE: 21.563
(i) It handles large amounts of input
data well. Makes quick predictions
after training.
(i) Slow training
Faiq et al.
[17]
(i) LSTM
(ii) LSTM-RNN
(iii) CNN-LSTM
(i) Daily data from 2018 to 2021,
from the Malaysian
Meteorological Department.
(i) LSTM: RMSE = 165 20;
MAE = 572 545.
(ii) LSTM-RNN: RMSE = 263 14;
MAE = 353 38.
(iii) CNN-LSTM: RMSE = 692 14,
MSE = 1134 1.
(i) Accurate prediction of building
energy consumption
(ii) Improved energy efficiency
(i) Requires a significant amount of
historical data to create an
accurate model
Bhol et al.
[29]
(i) ARIMA
(ii) Holt-Winters flower
pollination algorithm
(i) Laboratory-operated critical loads
over three months.
(i) HW-GFPA: MBE = 0 42 for
validation, 0.43 for test
RMSE = 0 80
(ii) ARIMA: MBE = 0 073 for
validation, 0.016 for test
RMSE = 0 183
(i) Scalability
(ii) Optimal hyperparameter
identification
(i) Sensitivity to kernel selection
6 International Journal of Energy Research

while skewness measures irregular probability distribution
around the mean value [33]. Equations (2) and (3) provide
formulas for skewness and kurtosis, which are essential for
understanding the data set distribution and its impact on
the prediction outcome.
Skewness = ∑N
i=1 xi−x3
N−1∗σ3,2
Kurtosis = ∑N
i=1 xi−x4
σ4,3
where nis the number of data points in the collection, xi
is the individual data points within the sample, and xis
the sample mean.
3.1.3. Feature Selection. Feature engineering is a crucial
aspect of machine learning, involving the creation of mean-
ingful data representations to enhance model performance.
It involves careful selection, transformation, and creation of
features that capture relevant information from raw data,
enhancing predictive accuracy and interoperability. Tech-
niques like principal component analysis, domain knowledge
extraction, and creative data manipulation help models
extract patterns and make accurate predictions, bridging
the gap between raw data and actionable insights.
As previously stated, our data set comprises 27 features
detailing the characteristics of the selected buildings. To
ensure optimal input for our predictive model, we employed
a feature engineering approach leveraging a tree-based
model, specifically the random forest algorithm.
3.2. Model Development. This study uses supervised machine
learning to predict energy usage using data prepared and
trained in two groups. The model employs regressive predic-
tion using random forest, LSTM, and gradient boosting
regressor. The process from data collection to model gener-
ation is depicted in Figure 3.
3.2.1. Random Forest. A random forest regressor is a
machine learning method that combines multiple decision
trees to create a predictive model for regression tasks. Each
tree is constructed using a randomly selected subset of train-
ing data and features with H x ;θk,k=1,⋯,Kwhere xrep-
resents the observed input (covariate) vector of length pwith
associated random vector X. During prediction, the regres-
sor aggregates predictions from all trees to generate the final
output, typically the average of the individual three predic-
tion h x = 1/k∑K
k=1h x ;θk[34]. This method is com-
monly used for pattern identification and prediction due to
its ability to learn complicated behavior, Consequently, it is
the best choice for constructing the prediction model in
the present study. In Figure 4, we present a flow chart of
the random forest algorithm.
3.2.2. Long Short-Term Memory. Sepp Hochreiter and Juer-
gen Schmidhuber introduced long short-term memory
(LSTM) in 1997 as an advanced application of recurrent
neural networks. LSTM is effective in processing and pre-
dicting time series data with varying durations. It captures
long-term relationships, handles variable-length sequences,
and recalls previous data, making it useful for energy con-
sumption prediction [35]. The LSTM model structure con-
sists of three layers: input, LSTM unit, and output. The
mathematical equations used in LSTM include the forget
gate, input gate, output gate, and cell state. The following
are the equations utilized in LSTM:
it=σWi·ht−1,xt+bift=σWf·ht−1,xt+bf,
Ct=ft·Ct−1+it·Ct,
ot=σWo·ht−1,xt+boht=ot· tan hC
t,
4
where xtis the input at the step t;it,ft, and otare the input,
forgot, and output vectors; gtis the candidate activation vec-
tor, and ctis the cell state at time t.
The LSTM algorithm is a powerful tool for collecting
and transmitting information across long sequences. It is
commonly used in applications such as audio recognition,
natural language processing, and time series analysis. Based
on previous research and the availability of a time series data
set, LSTM is chosen as the algorithm for predicting energy
with high precision. Figure 5 presents a flowchart of LSTM.
3.2.3. Gradient Boosting Regressor. The gradient boosting
approach is an iterative method that combines weak learners
to create a strong learner by focusing on errors at every step.
It is aimed at decreasing the loss function by finding an
approximation function of the function F x that translates
xto y. This method improves prediction performance and
lowers prediction error by matching weak learner models
to the loss function [36]. The squared error function is often
used to estimate the approximation function, which is then
used to find the ideal settings for weak learners. The gradient
boosting regressor’s mathematical equation is as follows:
yi=Fx
i+〠
M
m=1
γmhmx
i, 5
where yiis the predicted target, xiis the input features, F xi
is the ensemble model prediction, Mis the weak model, γm
is the learning rate, and hmxiis the prediction by m−th
weak model. The current research utilized gradient boosting
due to its robust predictive performance, ability to capture
complex data linkages and nonlinear patterns, and flexibility
and customization capabilities. Figure 6 depicts the gradient
boost regressor algorithm’sflow chart.
3.3. Model Evaluation. The data set was divided into a train-
ing group (25%) and a testing group (75%). The training
group was used to train machine learning algorithms and
create predictive models for maximum consumption data.
The testing group was used to evaluate the performance of
these models. This process is illustrated in Figure 7.
The training and testing process involved a simple parti-
tioning of data to prevent overfitting. Machine learning algo-
rithms’predictive models were evaluated for performance
7International Journal of Energy Research

and accuracy using metrics like R2, MSE, MAE, RMSE, and
MAPE. Each measurement definition is mentioned in
Table 2.
The present research used MSE because of its sensitivity
to errors, differentiability, and simplicity of interpretation.
The use of RMSE is preferable to MSE because it yields a
more easily understandable outcome in the original units
of the dependent variable, facilitating straightforward com-
parison across data sets or models. The mean absolute error
(MAE) is a suitable metric where the quantity of errors is
more significant than the specific direction of the mistakes,
offering a clear and direct evaluation of the model’s perfor-
mance, and MAPE is particularly valuable for comparing a
model’s prediction accuracy to the scale of the actual values.
Data
preparation
Training data
(70%) Random forest
Long short term
memory
Gradient boosting
regressor
Learning
algorithm
Predictive
model
Figure 3: Process of generating predictive model after data preparation.
Start
Input data
Random subset sampling
Feature subsampling
Construct trees
Prediction by trees
Aggregation
Final prediction
End
Figure 4: Random forest algorithm flowchart.
Start
Input (xt)
Memory cell
Forget gate (ft) Input gate (it)
Output gate (ot)
Hidden state (ht)
End
Candidate cell (gt)
Figure 5: Long short-term memory algorithm flowchart.
No
Yes
Calculate pseudo-residuals
Fit weak learner
Start
Input dataset
Initialize ensemble
For each iteration
Final prediction
End
Aggregated predictions
Update ensemble
Figure 6: Gradient boosting regressor flow chart.
8 International Journal of Energy Research

4. Results and Discussion
The experiment results were reviewed in sections, discuss-
ing the initial processing and imputation of missing data,
energy consumption prediction for each building, and per-
formance comparisons for random forest, long short-term
memory, and gradient boosting regressor models. The pre-
sentation of results follows a hierarchy, starting with the
normality test, then data preprocessing, and finally model
evaluation.
4.1. Normality Testing of Data. The evaluation is aimed at
examining the impact of data shape on predictive model
development performance, using measures of skewness and
kurtosis. Results were compiled in Table 3 to evaluate the
data’s shape and potential deviations from normal distribu-
tion. To evaluate the normality of the energy demand data,
the two values were computed using the aggregated data
from each building spanning from January 2020 to January
2023. Figure 8 also depicts the format of the data set for a
graphical examination of normality.
Based on Table 3, the data sets for the CLAS, NHAI, and
Cronkite buildings were approximately symmetrical and
skewed with bidirectional shape distribution. However, there
were some differences in the skewness values for each build-
ing. The CLAS building showed normal asymmetry due to
power consumption and KWS, with a slightly negative skew-
ness indicating a longer left tail. The CHWTON distribution
was skewed, witha skewnessof 427578, indicating a longer left
tail. The nursing and health innovation building had a pro-
nounced asymmetry, with power consumption having a posi-
tive skewness and KWS and CHWTON having a negative
skewness, indicating balanced tails. The Cronkite building
had positive skewness values, indicating a moderate right-
skewed distribution. Overall, all three data sets were approxi-
mately symmetric, skewed, and bimodal in their form density.
The kurtosis values of all three buildings in Table 3
were less than 0, indicating that their distributions were
Data
preparation
Generate
predictive values Comparison with
the actual
recorded
maximum
consumption
Training data
(30%) Root mean square
error
Mean absolute
error
Mean squared
error
Mean absolute
percentage error
Figure 7: Testing procedure for the trained predictive model.
Table 2: Performance metrics.
Algorithms Description Math form
R-squared [37]
The coefficient of determination is used to determine
how much of the variance in the dependent variable can
be explained by the independent variables.
R2=1−SSres
SStot
Mean squared error [38] A regression metric used to calculate the average squared
difference between predicted and actual values. MSE = 1
n〠
n
i=1
yi−yi
2
Root mean squared error [39]
RMSE is a widely used measure for estimating the
average variance between predicted and real values in
regression tasks.
RMSE = 1
n〠
n
i=1
yi−yi
2
Mean absolute error [40]
A regression statistic used to calculate the average
absolute difference between predicted and actual
values, ignoring the direction of mistakes.
MAE = 1
n〠
n
i=1
yi−yi
Mean absolute percentage error [39]
A commonly used method for determining forecasting
error, as it measures the average absolute percent inaccuracy
for each time period less actual values divided by actual
values, making understanding it simpler due to its scaled units.
MAPE = 1
n〠
n
i=1
yi−yi
yi
× 100
9International Journal of Energy Research

platykurtic. This was also evident in Figure 8, where the
probability distribution plot had a higher tail and a larger
peak center. However, the Cronkite building had a kurto-
sis value greater than 0, indicating a leptokurtic distribu-
tion with higher variance. CLAS and NHAI had roughly
normal distributions, but CLAS had a lower mean than
the median. Department CLAS also had an almost normal
distribution but with higher skewness and kurtosis. The
CHWTON data set had a higher variation compared to
the other data sets.
Table 3: Measurements of skewness and kurtosis for the buildings.
Application area Building name Power consumption KWS CHWTON
Skewness
CLAS -0.10206 -1 0.42329
NHAI 0.017914 -1 -1
Cronkite 0.805914 -1 0.494
Kurtosis
CLAS -0.333548 -2.0 -1.019492
NHAI -0.777519 -2.0 -2
CRONKIT 2.620576 2.056333 -0.687182
0.0000
4000
0.0001
0.0002
0.0003
0.0004
Density
0.0005
0.0006
0.0007
0.0008
5000 6000 7000 8000
0.00000
–1000
0.00005
0.00010
0.00015
Density
0.00020
0.00025
0.00030
0 1000 2000 3000 4000 5000 6000 7000
0.0000
2000
0.0001
0.0002
0.0003
Density
0.0004
1000 3000
Power_consumption (KW) CHWTON
4000 5000 6000 –0.4
Density
30
25
20
15
10
5
0
–0.2 0.0 0.2 0.4
0.0000
0.0004
0.0002
0.0006
0.0008
Density
0.0010
Power_consumption (KW) CHWTON
4000 5000 6000 7000 8000 0
Density
0.00035
0.00030
0.00025
0.00020
0.00015
0.00010
0.00005
0.00000
2000 4000 6000 8000
Figure 8: Probability density for buildings CLAS, NHAI, and Cronkite.
10 International Journal of Energy Research

4.2. Data Preprocessing. Based on Figure 9, the original data
set had various scale ranges for power consumption factors
like KWS, CHWTON, voltage, and building occupants. To
verify the prediction capacity of 29 features, multiple
approaches like correlation analysis, ensemble analysis, and
tree-based models were used. After testing the mentioned
methods, the most ideal qualities for projecting energy
demand and consumption are as follows:
(1) Previous consumption patterns
(2) Calendar: weekday, month, and season
(3) Demography: A building’s population might influ-
ence consumption patterns
(4) Geographical factors such as climate. People will use
more electrical appliances at hot and low tempera-
tures, respectively
The study on missing data utilized the missingness
matrix to quantify the extent of missing data and identify
rows that contained missing values. Upon analyzing
Figure 10, it is noteworthy that none of the three data sets
exhibited any missing data.
<bound method DataFrame.info of campus bldgno
0 Downtown 309 Beus center for law and society 2020 1
2020 1
2020 1
2020 1
2020 1
2022 12
2022 12
2022 12
2022 12
2023 1
. . .. . . . . .
1 Downtown 309 Beus center for law and society
2 Downtown 309 Beus center for law and society
3 Downtown 309 Beus center for law and society
4 Downtown 309 Beus center for law and society
1092 Downtown 309 Beus center for law and society
1093 Downtown 309 Beus center for law and society
1094 Downtown 309 Beus center for law and society
1095 Downtown 309 Beus center for law and society
1096 Downtown 309 Beus center for law and society
. . . . . . . . . . . .
Day Hour KW KWS CHWTONgalsgas HTmmBTU#Houses \
\
HTmmBTUlightbulbs HTmmBTUgalsgas Total#Houses Totallightbulbs
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
8905
9162
9651
10861
10305
2.550
2.702
2.710
2.452
2.467
4
5
6
7
1
2020-01-01T00:00:00.000
2020-01-02T00:00:00.000
2020-01-03T00:00:00.000
2020-01-04T00:00:00.000
2020-01-05T00:00:00.000
2020-12-28T00:00:00.000
2020-12-29T00:00:00.000
2020-12-30T00:00:00.000
2020-12-31T00:00:00.000
2020-12-32T00:00:00.000
1092
1093
1094
1095
1096
1092
1093
1094
1095
1096
11252
10343
12972
13775
10026
2.669
2.567
2.558
2.346
2.508
4
5
6
7
1
112759
124047
118887
126654
131700
43
47
45
48
50
3232
3167
3446
3504
3071
807960
791672
861333
875937
767612
135532
129250
132119
127761
119682
52
49
51
49
46
3129
3283
3360
3380
3282
782052
820649
839804
844761
820307
1
2
3
4
5
5364.07
5902.25
5915.77
5496.93
5512.42
–0.01
–0.01
–0.01
–0.01
–0.01
101
103
109
124
117
542
517
529
511
479
. . .
. . .
. . .
. . .
. . .
. . .
1092 28 4998.70 –0.01 129 451. . .
1093 29 5012.78 –0.01 118 496. . .
1094 30 4900.29 –0.01 150 476. . .
1095 31 4631.51 –0.01 160 507. . .
1096 1 4694.85 –0.01 115 527. . .
. . . . . . . . . . . . . . . . . . . . .. . .
. . .
. . . . . . . . . . . . . . .
. . . . . .. . . . . .
Totalgalsgas GHG DOW tstamp2
Figure 9: Summary of transform data set for CLAS building.
11International Journal of Energy Research

4.3. Feature Selection. Selecting the most crucial features
plays a vital role in enhancing the effectiveness, stability,
and scalability of our prediction model. Through the utiliza-
tion of a feature importance assessment method, as summa-
rized in Table 4, we identified the top five influential
features: KW, KWS, CHWTON, total houses, and
CHWTONgaslas. The ranking of these features is illustrated
in Figure 11, which shows the order of their importance.
Although the initial analysis considered all 29 parameters,
the figure only highlights features that significantly contrib-
ute to precision, ensuring a streamlined and informative
depiction.
The study is aimed at predicting energy consumption in
three educational buildings by identifying key parameters.
Through feature selection, we have identified key parameters
that significantly impact energy usage. These include
“CHwton”or chilled water tons which measures the cooling
capacity of chilled water systems, representing the heat
energy required to melt one ton of ice in 24 hours. Addition-
ally, “KW”denotes the power consumption of electrical
equipment and lighting systems within the buildings. “Total-
lightbulb”denotes the aggregate number of light bulbs or
lamps within the buildings, crucial for various assessments.
Furthermore, aspects of HVAC systems, like “CHWTON-
galsgas,”offer insights into chilled water and gas usage.
Moreover, “Combined mmBTU”measures the heat required
to raise the temperature of water by one degree Fahrenheit.
The feature selection process helps identify the most influen-
tial parameters for the predictive model, enabling more
accurate energy consumption forecasts.
4.4. Performance Evaluation and Comparison. The predic-
tion models’performance was evaluated by comparing mul-
tiple methods for each building after training and testing.
Comparative results are shown in Table 5.
Based on the performance evaluation measurements pre-
sented in Table 5, the GBR method exhibited outstanding
performance across all buildings. Notably, the determination
coefficients were remarkably high, reaching 0.998 for Cron-
kite, 0.984 for CLAS, and 0.845 for NHAI. Furthermore,
the corresponding mean squared error (MSE) values were
8.148, 5.09, and 9.17, respectively. The root mean squared
error (RMSE) and mean absolute error (MAE) also sup-
ported these results, indicating that GBR outperformed
other methods and yielded the best values. Additionally,
when assessing the mean absolute percentage error (MAPE)
results, GBR surpassed the other methods, demonstrating
the lowest error percentage. The LSTM method exhibited
lower determination coefficients compared to the GBR
results, with values of 0.86 for CLAS, 0.7772 for NHAI,
and 0.7609 for Cronkite. However, when comparing LSTM
to the RF method, the performance varied across buildings.
1.0 1097
Time reviewed
0.8
0.6
0.4
0.2
0.0
1097
877
658
438
219
0
1097
Power_consumption (KW)
1097
KWS
1097
CHWTON
1097
Weekday
Figure 10: Missingness graph of CLAS building.
Table 4: Feature importance.
Features Importance
CHWTON 0.303456
CHWTONgalsgas 0.23327
Total houses 0.291235
KW 0.353657
HTmmBTU 0.083478
Combined mmBTU 0.183562
HTmmBTUgalsgas 0.285535
Total light bulbs 0.229835
12 International Journal of Energy Research

Specifically, in the Cronkite building, the random forest
method outperformed LSTM with an R2 value of 0.89. Nev-
ertheless, in terms of other metrics such as MSE and RMSE,
LSTM yielded comparably smaller values than the RF
method. Moreover, there was a significant difference in the
MAPE results, with LSTM generating fewer errors compared
to random forest. This observation suggests that, in terms of
errors, LSTM performed better and produced a lower num-
ber of errors compared to RF. According to the forecast eval-
uation, the square error method was deemed a more suitable
evaluation metric for assessing the accuracy of the predic-
tions. Following this examination, it became clear that the
gradient boosting regressor (GBR) method performed the
best across all buildings.
Considering the data presented in Table 6, it is evident
that the algorithm closest to the real testing values is the gra-
dient boosting regressor, demonstrating good precision. The
Feature ranking
Feature
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
KW KWS CHWTON HTmmBTU Combined mmBTU Totalgalsgas Totallightbulbs Total#Houses CHWTONgalsgas HTmmBTUgalsgas
Importance
Figure 11: Feature importance.
Table 5: Predictions for performance evaluation using trained models.
Building Method R2 MSE RMS MAE MAPE
CLAS RF 0.8506 27.245 16.530 219.73 76.947
CLAS LSTM 0.8669 11.0921 13.3036 79.0677 56.0298
CLAS GBR 0.984 8.148 9.335 71.722 40.2587
NHAI RF 0.479 56.293 20.439 47.78 8.45123
NHAI LSTM 0.8372 27.10199 19.2844 33.0788 48.11382
NHAI GBR 0.795 15.089 17.4370 32.675 52.57254
Cronkite RF 0.89318 19.821 10.8491 117.704 56.54793
Cronkite LSTM 0.76096 26.12360 7.3153 29.09945 64.8606
Cronkite GBR 0.99817 9.1734 4.04234 16.3405 36.34167
Table 6: Real and predicted average consumption for each method.
Real values GBR test LSTM test RF test
5364.07 5511.33 5646.09 5666.75
5902.25 5881.72 5811.137 5822.69
5915.77 5900.722 5871.49 5870.67
5496.93 5491.65 5499.29 5496.55
5512.42 5523.29 5535.02 5535.18
6173.30 6178.63 6366.57 6354.28
6141.73 6296.113 6345.22 6365.09
6302.20 6364.17 6371.89 6389.07
6182.52 6182.480 6234.04 6240.37
6251.62 6171.9 6166.97 6168.348
6251.62 5606.99 5530.53 5527.52
5602,05 5626.398 5637.729 5527.52
5678,72 6769.29 6437.53 5641.79
6842,53 6893.64 6884.76 6417.95
6980,2 6701.16 6606.624 6876.06
6767,83 6695.89 6520.20 6622.32
6568,83 6394.089 6358.6 6506.90
5789,52 5730.96 5596.32934 6355.81
Table 7: Cross-validation score for models.
Algorithm RF LSTM GBR
Validation score 0.83 0.92 0.95
13International Journal of Energy Research

long short-term memory (LSTM) method follows in second
place, and the random forest algorithm comes last in terms
of accuracy in predicting average consumption. In the con-
text of result validation, K-fold cross-validation is a highly
suitable technique for our case due to its inherent advan-
tages. By partitioning the data set into K subsets, each con-
taining a representative sample of the data, K-fold cross-
validation ensures thorough training and validation of the
model. This approach maximizes data utilization and mini-
mizes bias, as every data point is utilized for both training
and validation across different folds. Furthermore, the aver-
aging of performance metrics over multiple splits provides a
robust evaluation, effectively reducing the variance associ-
ated with a single train-test split. Additionally, K-fold
cross-validation facilitates better generalization by assessing
the model’s performance across diverse subsets of the data,
ensuring that it can effectively handle various scenarios. Its
utility extends to hyperparameter tuning, enabling the com-
parison of different parameter configurations across multiple
validation sets.
In our scenario, we choose 5-fold cross-validation for its
moderate data set size, balancing computational efficiency
and robust performance estimation. This method ensures
reliable model evaluation without excessive computational
overhead and aligns with common practices in the field,
allowing easier comparison with existing literature and
benchmarks. Table 7 provides the outcome of the 5-fold
cross-validation.
A line graph comparison was used to better demonstrate
the difference between the actual and anticipated average
consumption levels, as depicted in Figures 12–14. In addi-
tion, Figures 15–17 show the graphical presentation of the
regression line for the three buildings. In the CLAS and
Cronkite buildings, the gradient boosting regressor (GBR)
produces a symmetric regression line, indicating that its
predicted values closely align with the actual ones. Con-
versely, for the NHAI building data set, characterized by
nonsymmetrical data, long short-term memory (LSTM)
outperforms other models due to its ability to capture tem-
poral dependencies.
However, in the case of NHAI, the performance differ-
ence between LSTM and GBR is minimal, highlighting the
suitability of both algorithms for different data characteris-
tics. GBR excels in all cases, while LSTM’s recurrent nature
makes it valuable for handling nonlinear, time-dependent
data.
7000
6500
6000
5500
5000
4500
0 50 100 150 200 250 300
1.0
0.8
0.6
0.4
0.2
0.0
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0.4
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0.0
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6500
6000
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4500
0 100 200 400 600 700 800
7500
4000
7000
6500
6000
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4500
7000
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6000
5500
5000
4500
7500
4000
300 500
0 50 100 150 200 250 300 0 100 200 400 600 700 800300 500
0 50 100 150 200 250 300 0 100 200 400 600 700 800300 500
Observed
Predicted_GBR
Observed
Predicted_LSTM
Observed
Predicted_RF
Figure 12: Real and predicted average consumption for CLAS building.
14 International Journal of Energy Research

From the analysis of all the tables and figures, we con-
clude that the best performances are consistently achieved
by the gradient boosting regressor (GBR). GBR’s sequential
training approach trains weak learners sequentially, correct-
ing errors from previous iterations, and fine-tuning the
model’s predictive capabilities with each step. Additionally,
gradient descent optimization minimizes prediction errors,
leading to more accurate predictions. Following GBR, long
short-term memory (LSTM) stands out as it is specifically
designed for handling sequential data, making it well-
suited for time series forecasting and similar tasks. Its ability
to understand and process temporal patterns contributes to
accurate predictions in time-dependent scenarios. Lastly,
the random forest algorithm also delivers good results, par-
ticularly when it comes to capturing complex nonlinear cor-
relations between features and the subject variable, and its
ability to model complex interactions and patterns makes
it effective.
The CLAS building has a significantly higher energy con-
sumption rate, exceeding 30 kWh, in contrast to the other
buildings. The main reason for this difference is the large sur-
face area and the simultaneous use for many educational
objectives. On the other hand, the Cronkite building has an
energy consumption rate of 26 kWh/h, while NHAI has a
consumption rate of 12 kWh per hour. Predictive modeling
approaches are necessary for efficient energy allocation and
management. Within this particular instance, the gradient
boosting regressor model demonstrates its superiority in
effectively predicting outcomes for both the CLAS and Cron-
kite buildings. The choice is backed by the model’s remark-
able performance metrics, as shown by its coefficient of
determination (R-squared) values of 0.99 for Cronkite and
0.98 for CLAS. This model improves the accuracy of forecast-
ing by offering proactive insights into the energy needs of
each building. It also helps in preventing energy loss before
it happens and promotes efforts to reduce energy usage.
5. Comparison with the Previous Study
The study compared three algorithms: random forest,
LSTM, and gradient boosting regressor, revealing their per-
formance in forecasting monthly average consumption.
8000
7500
7000
6500
6000
5500
1.0
0.8
0.6
0.4
0.2
0.0
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8000
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6000
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5000
8000
7500
7000
6500
6000
5500
8000
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6000
4000
5000
0 50 100 150 200 250 300 0 100 200 400 600 700 800300 500
0 50 100 150 200 250 300 0 100 200 400 600 700 800300 500
0 50 100 150 200 250 300 0 100 200 400 600 700 800300 500
Observed
Predicted_LSTM
Observed
Predicted_RF
Observed
Predicted_GBR
Figure 13: Real and predicted average consumption for Cronkite building.
15International Journal of Energy Research

The development of prediction models demonstrated their
capabilities, urging further optimization. The findings also
led to a comparative analysis with previous machine learn-
ing studies. In the first research conducted by Khaoula
et al. in 2022 [40], four machine learning algorithms were
implemented to predict energy demand for a commercial
building over two years. The algorithms used were multiple
linear regression (MLR), long short-term memory (LSTM),
simple linear regression (LR), and random forest (RF). The
results indicated that LSTM performed the best, followed
by RF, MLR, and LR, providing valuable insights into the
regression algorithms’capabilities. In the second research,
Khaoula et al. in 2023 [41] examined energy consumption
prediction in a low-energy house over four months. Unlike
the first research, this time, the prediction considered not
only the house’s energy but also its appliances. Three
machine learning algorithms, namely, artificial neural net-
works (ANN), recurrent neural networks (RNN), and ran-
dom forest (RF), were employed for tests. Recurrent neural
networks especially LSTM once again outperformed the
other algorithms, achieving an impressive accuracy of 96%.
RF was followed with 88% accuracy. However, ANN yielded
negative predictions, indicating its unsuitability for time
series data sets. Furthermore, in their research, Khaoula
et al. [42] used three deep learning algorithms—recurrent
neural networks (RNNs), artificial neural networks (ANNs),
and autoregressive neural networks (AR-NNs)—to forecast
the total load of HVAC systems. The results showed that
the autoregressive neural network model outperformed the
other two due to its ability to capture temporal dependencies
and patterns in time series data, which is crucial for HVAC
load prediction. AR-NNs use a simpler architecture, focus-
ing on past observations to predict future values, and their
autoregressive nature allows them to effectively model the
self-dependence of time series data, leading to more accurate
predictions.
Drawing insights from these three studies, significant
findings emerge regarding the efficacy of regression algo-
rithms for energy consumption prediction. Specifically, long
short-term memory (LSTM) and random forest (RF) consis-
tently emerge as top performers, especially in handling time
series data. However, our research introduces a novel aspect
by exploring the effectiveness of gradient boosting regressor
(GBR), which yielded exceptional results. Notably, GBR
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Predicted_RF
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Figure 14: Real and predicted average consumption for NHAI building.
16 International Journal of Energy Research

achieved remarkable precision, boasting an impressive accu-
racy of 98.9%. Moreover, compared to other algorithms,
GBR demonstrated superior performance with fewer errors,
as evidenced by lower root mean square (RMS), mean abso-
lute error (MAE), and mean absolute percentage error
(MAPE) values. This underscores the potential of GBR as a
formidable contender in energy consumption prediction
tasks, offering a promising alternative to LSTM and RF in
certain contexts.
6. Perspectives and Future Work
For future contributions, we plan to optimize the GBR
model by increasing the data used for training and predic-
tion, which may improve efficiency and performance on
larger data sets. We intend also to apply a novel approach
to the gradient boosting optimizer to fine-tune the model’s
parameters and hyperparameters more effectively. These
efforts are aimed at enhancing the GBR algorithm’s perfor-
mance for accurate energy consumption forecasting and
other applications.
Another significant contribution of our future research
lies in the utilization of transformer models for predicting
diurnal energy consumption patterns. Transformers, origi-
nally designed for natural language processing tasks, have
shown remarkable capabilities in capturing long-range
dependencies in sequential data, making them well-suited
for time series forecasting tasks as well. By applying trans-
former architectures to predict diurnal energy consumption,
we aim to leverage their ability to effectively model complex
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Figure 15: Regression line between observation and predictions for CLAS building.
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Figure 16: Regression line between observation and predictions for Cronkite building.
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Figure 17: Regression line between observation and predictions for NHAI building.
17International Journal of Energy Research

temporal patterns and dependencies inherent in energy con-
sumption data. Our case study focuses on commercial and
institutional buildings, where accurate energy consumption
prediction is crucial for optimizing building operations,
reducing costs, and minimizing environmental impact.
7. Conclusion
Our major focus in this research is developing an energy
consumption forecasting model given the environment of
three institutional buildings that have adopted the smart
building ecosystem. From January 2020 to January 2023,
the collected energy consumption data was subjected to sta-
tistical analysis to assess its normality. The skewness and
kurtosis values showed that the data had a variety of distri-
bution characteristics.
The predictive model development process involved data
preprocessing, which included handling missing data and
identifying feature importance. For this research’s objective,
three supervised machine learning methods, namely, gradi-
ent boosting regressor (GBR), long short-term memory
(LSTM), and random forest (RF), were selected as the algo-
rithms for the predictive model. The comparison of these
strategies was based on an assessment of their production
structures and prediction abilities. The results of our model
training and testing indicated that each strategy performed
differently for each building. Remarkably, the GBR approach
continually produced the most promising outcomes,
cementing its position as the best-performing strategy across
all three buildings: CLAS, NHAI, and Cronkite. GBR’s mean
absolute percentage error (MAPE) values were 9.337, 12.338,
and 4.045 for CLAS, NHAI, and Cronkite, respectively.
Additionally, GBR achieved a lower mean absolute error
(MAE) for CLAS and Cronkite (71.04 and 53.77, respec-
tively), while RF and LSTM yielded lower MAE results for
these two buildings. Moreover, while computing average
consumption using demand data, it was shown that the gra-
dient boosting regressor (GBR) displayed greater accuracy in
anticipating demand. This performance outperformed all
other approaches in all buildings.
In terms of future study recommendations, it is sug-
gested to use more powerful computers or platforms to run
the LSTM algorithm, potentially improving its performance.
Additionally, exploring hybrid or ensemble methods may be
beneficial, as they have shown higher accuracy than single
regressors. Lastly, a comparison with another smart building
could be included to distinguish and validate the obtained
results. These recommendations can further enhance the
understanding and applicability of the energy consumption
predictive model.
Data Availability
The collected data was saved in an open-source website
server [43] and could be manually downloaded from the
platform’s website in the form of a CSV file with any sort
of aggregation [43] (https://portal.emcs.cornell.edu/d/2/
dashboard-list?orgId=2).
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
The authors thank the National Center for Scientific and
Technical Research (CNRST) for supporting and funding
this research.
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