A COMPREHENSIVE REVIEW OF ENERGY LOAD MANAGEMENT AND DEMAND

RESPONSE STRATEGIES

Antonette B. Capara, Craig David I. Escario

Keith Jewel S. Maullon, Lheiann R. Perido
Department of Computer, Electronics, and Electrical Engineering
College of Engineering and Information Technology
Cavite State University-Main Campus

ABSTRACT
This case study reviews strategies for managing and forecasting
energy demand in modern power systems, highlighting research on
user behavior in heating, load patterns in commercial buildings, and
scheduling challenges with renewable energy integration. Studies
show that differences in user preferences, such as varying
responses to temperature changes, complicate load predictions and
energy management. Big data and smart technologies, including IoT
devices and advanced metering, enable more accurate monitoring
and forecasting by capturing real-time usage and environmental
factors. Additionally, flexible scheduling methods and demand
response strategies can stabilize energy systems during peak
demand and reduce infrastructure costs by balancing load
fluctuations. This case study provides a framework for improving
energy management efficiency, supporting a more reliable and
sustainable energy grid.
Keywords: Demand response mechanisms, energy management
strategies, load forecasting, user-behavior analysis, electrical
heating load analysis, peak demand management, sustainability in
energy management.

INTRODUCTION
Effective management of energy demand ventilation, and air conditioning (HVAC)
and precise load forecasting are becoming systems. Techniques like neural networks
essential due to the growth of and ensemble algorithms have shown
interconnected energy systems and the success in forecasting energy demands
shift toward sustainable practices. more accurately for residential and
Traditional energy models, limited in commercial buildings, leading to cost
precision and adaptability, are now being savings and reductions in carbon
strengthened by data-driven methods that emissions (Abdel-Jaber & Dirks, 2024).
better capture demand variations across
different settings—from homes to In industrial energy systems, particularly
industrial parks. those in industrial parks, managing the
interconnected needs for electricity, heat,
A key area in recent research focuses on and gas has inspired advanced
using machine learning (ML) to predict approaches such as deep multitask
energy needs, especially for heating, learning. This approach enables
simultaneous forecasting of multiple
energy sources, ensuring efficient load To refine the extensive list of references,
distribution and stable networks. Such the following criteria were applied:
models are valuable for improving system 1. Documents published from 2018
resilience and flexibility as renewable onward to ensure the inclusion of
energy becomes more common (Zhang et the most current research relevant
al., 2020). to the study.
2. Specific keywords related to load
Additionally, smart building energy characteristics and demands in
management systems (BEMS) use IoT distribution systems were used to
and sensors to adjust HVAC usage in real filter the search results effectively.
time, based on occupancy and weather 3. Studies focusing on practical
conditions. This adaptive control allows for applications or case studies within
better energy efficiency, helping reduce real-world distribution systems, to
waste and optimize system performance provide actionable insights and
(Verwiebe et al., 2021). relevance to industry needs.

Altogether, these advancements highlight Documents Reviewed

the importance of load management
analysis and demand-response strategies, The four documents reviewed represent a
and smart technology in building a more range of sources addressing energy load
efficient, flexible, and sustainable energy characteristics and demand management.
system. This topic review will explore Each study focuses on elements like user
these aspects to develop a holistic behavior modeling, big data analysis, and
framework for optimizing energy demand load scheduling flexibility. Methods used
and load management strategies in fall into two categories: behavioral
today’s interconnected energy landscape. modeling and quantitative analysis. For
instance, Wang et al. (2020) modeled
METHODOLOGY heating loads based on user behavior,
while Qian et al. (2018) analyzed
The researchers utilized a variety of commercial building load patterns using
databases and search terms to collect big data. Other studies evaluated load
relevant literature on the topics of interest, scheduling potential (Li et al., 2023) and
including: demand management frameworks
● ScienceDirect Publication (Motahari & Rahimpour, 2024). Together,
Database these studies support enhanced energy
● IEEE Xplore Digital Library system reliability and efficiency amid
● IOPscience Digital Library evolving power demands.

Table 1. Summary of documents reviewed

TITLE OF THE AUTHOR/(S): SOURCE SIGNIFICANC ISSUES

STUDY TYPE E
Modeling of Wang, Z., Journal It examined Electrical heating
regional electrical Wang, H., Lin, Article electrical load modeling,
heating load Z., Liu, W., heating, including the
characteristics Mao, Y., Wang, particularly in variability in user
considering user X., & Wang, S. the context of response to
response behavior (2020) varying user temperature
difference behaviors and changes,
preferences. As differences in
energy heating
consumption preferences
 patterns among users,
evolved with and the impact of
the integration external factors
of smart such as weather
technologies conditions.
and renewable These factors led
energy to inaccuracies
sources, in load
understanding forecasting and
these dynamics complicated
was energy
fundamental for management
optimizing strategies.
energy
management
and ensuring a
reliable supply.
Analysis of electric Qian, T., Wang, Journal The study is The paper
load characteristics K., Li, Y., Guo, Article significant for tackles different
of commercial and X., & Liu, J. its potential to issues in energy
public buildings (2018) enhance management for
based on big data energy commercial and
efficiency, public
optimize peak infrastructure.
demand One major issue
management, addressed is the
and improve variability in
grid stability electricity
through better consumption
forecasting. It patterns across
provides various types of
essential data buildings, such
for energy as offices,
providers and schools, and
policymakers to hotels. These
develop tailored differences are
solutions for influenced by the
different type of activities
building types, and the
optimize load building’s
distribution, and operational
stabilize energy schedule.
grids during
peak times.
Additionally, it
emphasizes the
role of big data
in refining load
profiles, which
is crucial for
integrating
renewable
 energy and
advanced grid
technologies
into modern
energy
systems.

Evaluation Method Li, L., Liu, W., Proceedings It evaluates Load scheduling
of Load Scheduling Liu, Y., Huang, Article both the complexity in
M., Yu, H., & self-regulation power systems
Potential Based on
Wen, X. (2023) characteristics due to increased
Load of resources load-side
Characteristics and and the resources,
Flexibility flexibility of focusing on the
distribution need for
network nodes accurate
to formulate assessment of
feasible scheduling
scheduling potential and the
strategies, impact of load
ensuring better fluctuations on
management of distribution
load networks.
fluctuations.
Energy demand Motahari, S., & Book The study Managing peak
management Rahimpour, M. Chapter promotes energy demand
R. (2024 sustainable is a challenge,
energy which occurs
practices that when energy
can meet supply systems
current are under stress
demands to ensure system
without system reliability
depleting and efficiency.
resources, The study also
which is highlights the
essential for need to reduce
long-term investments in
energy security power grids and
and generating
environmental facilities by
sustainability. effectively
The study managing
emphasizes the energy
importance of consumption
integrating patterns.
renewable
energy sources
and demand
response
strategies into
the energy
system, leading
to a more
 resilient and
flexible energy
infrastructure.

Annotated References the accuracy of load forecasting. This

approach allowed for better energy
1. Wang, Z., Wang, H., Lin, Z., Liu, W., management strategies, helping utilities
Mao, Y., Wang, X., & Wang, S. (2020). meet demand while optimizing the
Modeling of regional electrical heating reliability and efficiency of electrical
load characteristics considering user heating systems.
response behavior difference.
International Journal of Electrical Power & Design considerations identified
Energy Systems, 123, 106297. ● User Behavior Modeling:
https://doi.org/10.1016/j.ijepes.2020.1062 Developing comprehensive models
97 that account for variations in user
behavior is crucial for accurately
Issues Addressed: predicting electrical heating loads.
Electrical heating load modeling, including This involves analyzing how
the variability in user response to different users respond to changes
temperature changes, differences in in temperature and their individual
heating preferences among users, and the heating preferences.
impact of external factors such as weather ● Data Collection and Analysis:
conditions. These factors led to Effective data collection methods
inaccuracies in load forecasting and are essential for capturing
complicated energy management real-time usage patterns and
strategies. external influences such as
weather conditions. The authors
Significance: stress the importance of utilizing
It examined electrical heating, particularly smart meters and IoT devices to
in the context of varying user behaviors gather detailed data that can
and preferences. As energy consumption inform load forecasting models.
patterns evolved with the integration of
smart technologies and renewable energy 2. Qian, T., Wang, K., Li, Y., Guo, X., &
sources, understanding these dynamics Liu, J. (2018). Analysis of electric
was fundamental for optimizing energy load characteristics of commercial
management and ensuring a reliable and public buildings based on big
supply. data. IOP Conference Series
Materials Science and
Engineering, 394(4), 042105.
Summary:
https://doi.org/10.1088/1757-899x/
The researchers examined how user
394/4/042105
response behavior affected electrical
heating load characteristics in various Issues Addressed:
regions. They used advanced modeling The paper tackles different issues in
techniques to consider differences in energy management for commercial and
individual heating preferences and public infrastructure. One major issue
responses to temperature changes, addressed is the variability in electricity
understanding that these factors had a big consumption patterns across various
impact on energy consumption patterns. types of buildings, such as offices,
By suggesting a framework that combined schools, and hotels. These differences are
user behavior with external factors like
weather conditions, it aimed to improve
influenced by the type of activities and the energy use, especially in buildings
building’s operational schedule. like hotels, where temperature
changes have a stronger effect
Significance: during workdays than holidays.
The study is significant for its potential to ● Need for Adaptive Forecasting
enhance energy efficiency, optimize peak Models and Advanced Metering:
demand management, and improve grid The findings suggest future energy
stability through better forecasting. It systems should use adaptive
provides essential data for energy forecasting models and advanced
providers and policymakers to develop metering infrastructure to capture
tailored solutions for different building the complexities of energy
types, optimize load distribution, and consumption better and improve
stabilize energy grids during peak times. grid efficiency.
Additionally, it emphasizes the role of big
data in refining load profiles, which is 3. Li, L., Liu, W., Liu, Y., Huang, M., Yu, H.,
crucial for integrating renewable energy & Wen, X. (2023). Evaluation method of
and advanced grid technologies into load scheduling potential based on load
modern energy systems. characteristics and flexibility.
https://doi.org/10.1109/ic2ecs60824.2023.
Summary: 10493680
The paper analyzed the electric load
characteristics of commercial and public Issues Addressed:
buildings using data from power The study addresses several key issues in
companies. It examines electricity modern power systems. First, it highlights
consumption based on time patterns, load the increased complexity of scheduling
change rates, and the impact of due to the integration of more load-side
temperature. It reveals that while many resources, which requires new methods
commercial and public buildings share beyond traditional approaches. Second,
similar patterns in terms of activity load fluctuations from these resources can
duration and energy usage types (e.g., air
cause operational challenges, such as
conditioning, lighting, office appliances),
voltage levels exceeding acceptable limits,
their specific daily load profiles differ
potentially affecting the stability of the
significantly. These differences underline
the need for customized energy power supply. Third, the study
management strategies. For example, underscores the importance of evaluating
office buildings typically show a steady the self-regulation of load-side resources
increase in energy use during working to determine their scheduling potential
hours, with a peak around mid-morning, without harming the distribution network.
followed by a decline after business hours. Additionally, assessing the flexibility of
In contrast, schools exhibit a distinct load distribution network nodes is crucial for
pattern, peaking during midday. The understanding how well the network can
findings are intended to provide theoretical handle load changes, enhancing its
guidance for future planning in energy resilience. Lastly, the bearing capacity of
markets, including load forecasting, supply the network is discussed, emphasizing its
planning, and peak demand management. role in ensuring that load fluctuations from
resource participation do not compromise
Design considerations identified
system reliability.
● Impact of Temperature on
Energy Demand: The research
Significance:
points out that temperature plays
an important role in influencing
It highlights the importance in improving ● Self-Regulation of Resources:
the overall reliability and efficiency of The paper considers the
power systems. By tackling the complexity self-regulation capabilities of
of scheduling, the study contributes to load-side resources, which are
better understanding and management of crucial for effective scheduling.
load-side resources. Managing load ● Network Bearing Capacity: The
fluctuations is needed for maintaining design must account for the
power supply stability, while optimizing distribution network's capacity to
resource self-regulation helps in creating handle load fluctuations without
more efficient scheduling strategies. exceeding voltage limits.
Furthermore, improving network flexibility
strengthens its resilience to load changes 4. Motahari, S., & Rahimpour, M. R.
and considering the network's bearing (2024). Energy Demand Management. In
capacity ensures the feasibility of Elsevier eBooks (pp. 33–44).
scheduling strategies in handling https://doi.org/10.1016/b978-0-323-93940-
increasing demand and resource 9.00248-6
variability.
Issues Addressed:
The study addresses several key issues
Summary:
related to energy demand management.
The paper addresses the complexity in
One major issue is managing peak energy
scheduling due to increased resources on
demand, which stresses energy supply
the load side of power systems. The paper
systems when demand surges. The study
emphasizes assessing the scheduling emphasizes the need for strategies that
potential of these resources to handle influence the timing and amount of energy
large load fluctuations without consumption to maintain system stability
compromising the stability of the during these periods. Another issue is the
distribution network. The proposed dual financial burden of investing in energy
characteristics evaluation method infrastructure. Effective demand
assesses the self-regulation of resources management can reduce the need for
and flexibility of network nodes. costly investments in power grids and
Simulations demonstrate that it yields generating facilities. The study also
realistic evaluations compared to highlights sustainability concerns,
traditional methods, contributing to stressing the importance of managing
optimizing load management in modern energy in a way that preserves resources
for future generations. Additionally, it
power systems.
discusses the challenge of integrating
renewable energy sources into existing
Design considerations identified
systems, essential for enhancing system
● Load Characteristics: The study
resilience and flexibility. Finally, the study
emphasizes the need to
addresses demand response
understand the characteristics of mechanisms, allowing consumers to
loads in the distribution network, adjust their energy usage during peak
which influence scheduling periods, benefiting both the energy supply
potential and flexibility. system and consumers.
● Flexibility of Nodes: It highlights Significance:
the importance of evaluating the The study's significance lies in promoting
flexibility of distribution network sustainable energy practices to meet
nodes to manage load fluctuations current demands without depleting
effectively. resources, ensuring future energy
availability. It highlights economic benefits conditions, which can help alleviate
by managing peak demand, helping peak demand issues .
utilities avoid costly infrastructure ● System Flexibility: It emphasizes
investments and lowering consumer the need for a flexible energy
energy costs. The study enhances energy system that can adapt to varying
system resilience through the integration energy demands and incorporate
of renewable sources and demand diverse energy sources effectively .
response strategies, ensuring reliability
● Sustainability: The design
amid fluctuating demands. Additionally, it
considerations also include
encourages technological innovation in
ensuring that energy management
energy management and empowers
consumers to actively participate in strategies are sustainable and can
managing their energy usage, fostering be reused repeatedly, aligning with
responsible and efficient energy the principles of sustainable
consumption. energy .

Summary: Synthesis
The study explores energy demand Studies, articles and book chapters
management strategies aimed at examined emphasize the complexities and
influencing the timing and quantity of significance of energy demand
energy consumption to reduce peak management across various contexts,
demand, alleviating stress on energy particularly concerning load characteristics
supply systems. Although managing peak and demands in distribution systems.
demand may not lower overall Wang et al. (2020) address electrical
consumption, it reduces the need for heating load modeling, focusing on user
significant investments in power response behavior, temperature
infrastructure, leading to more efficient preferences, and external factors that lead
resource use and cost savings for utilities to inaccuracies in load forecasting and
and consumers. The study emphasizes complicate energy management
sustainable energy practices, ensuring strategies. Qian et al. (2018) analyze the
long-term resource viability, and highlights electric load characteristics of commercial
the integration of renewable energy into and public buildings, revealing variability
current systems to enhance resilience. It in electricity consumption patterns driven
also promotes demand response by building types and operational
mechanisms, enabling consumers to schedules, which is crucial for optimizing
adjust their energy usage during peak energy management. Li et al. (2023)
periods, contributing to both system discuss the increased complexity in
efficiency and cost savings. Overall, the scheduling due to load-side resource
study offers a comprehensive framework integration, highlighting the importance of
for addressing the complexities of energy evaluating resource self-regulation and
management to create a sustainable and node flexibility to maintain stability in
efficient energy system for present and power systems. Lastly, Motahari and
future needs. Rahimpour (2024) explore energy demand
management strategies, emphasizing the
Design considerations identified need to manage peak demand, reduce
● Demand Response (DR): The financial burdens on infrastructure, and
study identifies demand response integrate renewable energy sources while
as a critical design consideration, enabling demand response mechanisms.
allowing for adjustments in energy Collectively, these studies illustrate the
consumption based on supply need for sustainable energy practices,
improved forecasting, and flexible energy
systems that enhance reliability and characteristics of commercial and
efficiency in distribution systems. public buildings based on big data.
IOP Conference Series Materials
RECOMMENDATIONS Science and Engineering, 394(4),
The researchers recommend the following 042105.https://doi.org/10.1088/175
notes in improving the analysis and review 7-899x/394/4/042105
of related literature about load
Li, L., Liu, W., Liu, Y., Huang, M., Yu, H., &
characteristics and demands in distribution
Wen, X. (2023). Evaluation Method
systems: of Load Scheduling Potential
● Perform comprehensive Based on Load Characteristics and
meta-analyses to synthesize Flexibility. Evaluation Method of
findings from existing studies to Load Scheduling Potential Based
identify trends and gaps in on Load Characteristics and
understanding load characteristics Flexibility.
and enhance demand https://doi.org/10.1109/ic2ecs6082
management strategies. 4.2023.10493680
● Incorporate insights from
behavioral economics, Motahari, S., & Rahimpour, M. R. (2024).
environmental science, and data Energy Demand Management. In
Elsevier eBooks (pp. 33–44).
analytics to provide a deeper
https://doi.org/10.1016/b978-0-323
understanding of factors -93940-9.00248-6
influencing load characteristics and
improve energy management. Verwiebe, P. A., Seim, S., Burges, S.,
● Focus on the impact of emerging Schulz, L., & Müller-Kirchenbauer,
technologies, such as smart grids J. (2021b). Modeling Energy
and AI, on load characteristics by Demand—A Systematic Literature
exploring how these innovations Review. Energies, 14(23), 7859.
optimize load forecasting and https://doi.org/10.3390/en1423785
distribution can provide valuable 9
insights for modern energy
systems. Abdel-Jaber, F., & Dirks, K. N. (2024). A
review of Cooling and Heating
Loads Predictions of Residential
LITERATURES CITED
Buildings Using Data-Driven
Wang, Z., Wang, H., Lin, Z., Liu, W., Mao, Techniques. Buildings, 14(3), 752.
Y., Wang, X., & Wang, S. (2020). https://doi.org/10.3390/buildings14
Modeling of regional electrical 030752
heating load characteristics
considering user response Zhang, L., Shi, J., Wang, L., & Xu, C.
behavior difference. International (2020). Electricity, heat, and gas
Journal of Electrical Power & load forecasting based on deep
Energy Systems, 123, 106297. multitask learning in Industrial-Park
https://doi.org/10.1016/j.ijepes.202 Integrated Energy System.
0.106297 Entropy, 22(12), 1355.
https://doi.org/10.3390/e22121355
Qian, T., Wang, K., Li, Y., Guo, X., & Liu, J.
(2018). Analysis of electric load