Practical Research on the Combination of Hydro Power Station
Dispatching and Artificial Intelligence Algorithms
Xiong Feng
Lanzhou Resources & Environment Voc-Tech University, Gansu Province, 730021, China
Keywords: Gradient Superposition Theory, Artificial Intelligence Algorithms, Residential Hydro Power Applications,
Industrial Hydro Power Station, Methodological Research.
Abstract: In order to meet the challenges of hydro power station dispatching practice research, in view of the
shortcomings of the existing breadth search algorithms, this study introduces an innovative hydro power
station dispatching practice research method based on artificial intelligence algorithms. This new scheme uses
the principle of gradient superposition theory to accurately identify and locate key influencing factors, and
accordingly carries out intelligent indicator classification work to reduce possible interference. At the same
time, using the unique mechanism of artificial intelligence algorithms, this scheme cleverly constructs the
design strategy of industrial hydro power stations. The empirical results show that the proposed scheme shows
a significant improvement compared with the traditional breadth search algorithm in terms of the accuracy of
hydro power station dispatching practice research and the processing efficiency of key factors, showing its
obvious strong advantages. In residential hydro power applications, hydro power station dispatching practice
research plays a crucial role, which can accurately predict and optimize the growth trend and output results
of hydro power station dispatching practice research. However, in the face of complex simulation tasks,
traditional breadth search algorithms show some inherent shortcomings, especially when dealing with multi-
level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a
new idea of hydro power station dispatching practice optimized by artificial intelligence algorithm, and
accurately controls the influencing parameters through gradient superposition theory, and uses it as a road
map for index allocation, and then uses artificial intelligence algorithm to innovate and construct a system
scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has
been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing
stronger performance superiority. Therefore, in the practical research of hydro power station scheduling, the
simulation scheme based on artificial intelligence algorithm successfully overcomes the shortcomings of the
traditional breadth search algorithm, and significantly improves the accuracy and operation efficiency of the
simulation.
1 INTRODUCTION
The importance of research on hydro power station
dispatching practice in residential hydro power
application is self-evident (Sun and Li, et al. 2023).
Through simulation, various parameters and changes
in this process can be predicted and understood,
providing guidance and support for actual production.
However (Xi and Yao, 2023), the traditional hydro
power station dispatching practice research scheme
has some shortcomings in terms of accuracy, which
limits its effect in practical application (Zhao, 2023).
In order to solve the problem of accuracy of
traditional hydro power station dispatching practice,
researchers have introduced artificial intelligence
algorithms into the research and analysis of hydro
power station dispatching practice in recent years
(Chen and Li, et al. 2023). Artificial intelligence
algorithms is a computational method based on group
behavior that simulates the interaction and
cooperation between individuals to achieve the goal
of global optimization (Liu and Deng, et al. 2023).
The algorithm has the characteristics of
decentralization, immutability and smart contract
(Xu, 2023), which can effectively solve the accuracy
problems existing in traditional schemes (Wang,
2023). The optimization model of hydro power
station dispatching practice research based on
Feng, X.
Practical Research on the Combination of Hydro Power Station Dispatching and Artificial Intelligence Algorithms.
DOI: 10.5220/0013551000004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 555-562
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
555

artificial intelligence algorithm further improves the
accuracy and reliability of simulation by optimizing
the parameters and algorithms in the research process
of hydro power station dispatching practice (Liu,
2023). The model adjusts and optimizes the various
parameters in this process to achieve the best results
for the industrial hydro power plant (Zhou, 2023). At
the same time, the model is able to cope with complex
environments and interference factors (Wu, 2023),
providing more realistic and reliable simulation
results. The researchers used a large number of
experiments and data analysis to evaluate the
effectiveness of the optimization model for hydro
power station dispatching practice research based on
artificial intelligence algorithms (Hou and Zhang,
2023). The results show that compared with the
traditional hydro power station dispatching practice
research scheme, the proposed model has significant
advantages in many aspects.
2 CONSTRUCTION OF A
THEORETICAL MODEL FOR
THE PRACTICAL STUDY OF
HYDRO POWER STATION
DISPATCHING
The artificial intelligence algorithm uses computer
technology to improve the research strategy of hydro
power station dispatching practice, and analyzes a
series is
B

of key parameters involved in the system
research to identify the parameter values is
s

that do
not meet the standards in the study. Subsequently, the
algorithm integrates these parameter values is
()
2
ss r σσ ⋅−
 
into the research scheme of hydro
power station dispatching practice, and then
comprehensively evaluates the implementation
possibility of the study. The calculation process can
be referred to equations (1) and (2).
(1)
(2)
The artificial intelligence algorithm combines the
advantages of computer technology and quantifies the
research on hydro power station dispatching practice,
which can improve the accuracy of hydro power
station dispatching practice (Li, 2023).
The artificial intelligence algorithm implements a
global search for the practical research on hydro
power station dispatching according to the set number
of iterations, and completes an iterative process for
each search (Yu, 2023). Pheromones will be
generated in the process of hydro power station
dispatching practice, so the remaining pheromones in
the search path need to be updated after each iteration
process, and the formula is described as follows:
(3)
In order to avoid falling into the local optimal
problem in the target iteration process, the upper limit
of pheromone value is
z
Φ
set, and the formula is
c
zz−
described as follows:
(4)
From the above, the comprehensive function of
the practical research on hydro power station
scheduling can be obtained, and the result is shown in
equation (5).
(5)
In order to improve the reliability of hydro power
station dispatching practice, it is necessary to
standardize all data, and the results is shown in
equation (6).
(6)
Before the artificial intelligence algorithm is
carried out, it is necessary to conduct a
comprehensive analysis of the research plan of hydro
power station dispatching practice, and map the
research requirements of hydro power station
dispatching practice to the resource query system
research database, and eliminate the unqualified
2
00
35
3
44
s
Br
ss
μμ
σ
σ
ππ
⋅

=− ∇ = −




R
()
22
20 1
ˆ
ˆ
rz
dx a b s re z u z e+=+−−

() ()
()
()
()
2
2
0
52
2
2
ˆ
ˆ
32
4
cr c z
c
rz ze r z z e
B
rzz
μσ
π


−−−−


=




+−



()
()
()
2
2
3
2
00
2
2
ˆ
r
zz
c
yr
Be rdrd
x
rzz
π
δ
θ
δ
Φ= = Χ
+−


()
dd
dd
aa
ec c
c
NN
uzz
θξ ξ
ΦΦ
=− =
−
()
2
2
0
2
,1
1
2
ij
c
e
ij
c
N
Av
ξμ σ
θ
+
=
∂Ω
=−
∂

INCOFT 2025 - International Conference on Futuristic Technology
556

resource query system research plan. The anomaly
assessment scheme can be proposed, and the results
is
(
i
N
otmu

shown in equation (7).
(7)
Hypothesis: The method of capturing the line
shape of any trajectory is
2
e
k
to analyze the
relationship between the input variables and the
output variables under constraints, is
()
ut
shown in
equation (8).
(8)
According to the above trajectory linear snapping
method, the continuous operator of the trajectory
linear snapping method is
ζ
obtained, and the
calculation result is
z
F
shown in equation (9).
(9)
where is the performance coefficient of the
trajectory line capture method, and B is the stratum.
According to the design results of the resource
management system, the output value of the resource
management system design can be obtained, as
shown in equation (9).
(10)
3 A PRACTICAL CASE STUDY
OF HYDRO POWER STATION
DISPATCHING PRACTICE
3.1 The Relevant Concepts of Hydro
Power Station Dispatching Practice
Research Model Construction
The construction of the hydro power station
dispatching practice research model contains several
key concepts to ensure that the model can not only
comprehensively map the complexity of the industrial
hydro power station process, but also show sufficient
applicability and accuracy. First of all, it involves the
thinking of systems theory, which emphasizes that
when shaping the model, it is necessary to conduct a
holistic examination of the mathematical, chemical
and physical elements involved in industrial hydro
power stations, and understand how these elements
interact and interact from a system perspective to
jointly affect the overall process of industrial hydro
power stations. Further, there is the concept of
dynamic evolution, which requires the model to
keenly reveal time-based dynamics and processes as
they continue to evolve over time, as well as to keep
up with the change and growth of activities. The
concept of multi-level modeling reveals that the
constructed model should incorporate the scale of
change in different fields from macro to micro, from
physics and mathematics to process flow, to ensure
that the model is compatible and covers different
levels of detailed information. The estimation and
verification of parameters is the key process to ensure
that the research model of hydro power station
dispatching practice truly reflects the actual search
process, and these parameters is determined and fine-
tuned through actual data to ensure that the model
results is consistent with the actual observations. The
data-driven principle further highlights the central
role of observational data in the model building and
validation stage, and the collection, processing, and
analysis of data constitute an indispensable part of
building accurate models. In addition, considering
that different industrial hydro power station scenarios
and different residential hydro power application
paths may require different model configurations, the
scalability of the model is particularly crucial, which
means that the model should be designed to be easy
to change and add new components to adapt to the
changing environment and needs of industrial hydro
power plants.
Based on the above concepts, the construction of
a practical research model for hydro power station
dispatching requires not only a thorough scientific
insight into the multidisciplinary process, but also a
wide range of system analysis perspectives, strong
data processing technology, and future-oriented open
thinking. The synergy of many elements can create a
simulation model of residential hydro power
application process that is both accurate and widely
applicable.
Simulate the research process of hydro power
station dispatching practice, as shown in Figure 1.
2
2
(
iz
No t mu F cu
v
∂Ω
=−−
∂
 
()
0
0
2
22
0
lim
2
r
t
r
ee
t
x
kk
πω
δ
ω
π
+
→
=Κ

()
2
2
z
npenr
F
uu
m
ωζ ζ ζ ω ++ + =

()
12
2
0
11
12
11
,lim
nn
zi j
x
ij
i
Brz
nn r
δ
π
→
==
=ΦΒ

Practical Research on the Combination of Hydro Power Station Dispatching and Artificial Intelligence Algorithms
557

Gradient
Project work
Artificial
Composition Practice
Coalescent
Application
Figure 1: The analysis process of the practical study of
hydro power station dispatching
Compared with the breadth search algorithm, the
introduction of artificial intelligence algorithm in the
practical research of hydro power station scheduling
has brought a lot of innovation to solve practical
problems. As a critical step in processing natural
language, accuracy is critical in understanding and
processing natural data in search. This algorithm can
better deal with the complexity of semantics and
syntax in industrial hydro power stations, so the
artificial intelligence algorithm has inherent
advantages over the traditional breadth search
algorithm in terms of the rationality and accuracy of
hydro power station scheduling practice. As shown in
Figure II, the use of AI algorithms can lead to higher
accuracy of search results, as the AI algorithms more
accurately parse the keywords and structures in the
user's search intent and achieve more detailed
information matching. compared with breadth search
algorithms, which often rely on preset rules and paths,
AI algorithms can process data more flexibly in the
face of complex searches, reducing
misunderstandings and ambiguities.
In terms of search speed, although the breadth
search algorithm searches quickly when the structure
is clear, the artificial intelligence algorithm can also
achieve fast and effective search feedback by
optimizing the cutting and matching process of
words, especially in the face of large-scale thesaurus
and dynamically updated search resources, the
artificial intelligence algorithm can maintain efficient
search ability. In terms of stability, AI algorithms is
able to respond to changing search environments and
usage patterns through continuous learning and self-
optimization, thereby providing a stable search
experience. However, due to the lack of learning
mechanism, the breadth search algorithm may need to
be redesigned and adjusted once it encounters a
change in search mode or a new data type, which is
slightly inferior in terms of stability. In practical
applications, AI algorithms can be combined with
other advanced machine learning techniques, such as
deep learning, semantic understanding, etc., to further
improve the overall performance and user experience
of hydro power station dispatching practice. As for
the breadth search algorithm, although it still has its
unique application scenarios in the search task with
clear rules and fixed rules, it is obvious that the
artificial intelligence algorithm provides a more
advanced and adaptable solution in the practical
research of modern hydro power station scheduling.
3.2 Research on the Practice of Hydro
Power Station Dispatching
When developing a design for an industrial hydro
power plant system, it is important to note that the
scheme should cover all types of data. We categorize
this data into unstructured, semi-structured, and
structured information, each with its own
characteristics and methods of storage, processing,
and analysis. Using efficient artificial intelligence
algorithms, we were able to efficiently conduct a
preliminary screening of these diverse data types to
obtain a preliminary selected set of research schemes
for hydro power station dispatching practices. After
the screening of artificial intelligence algorithms, we
obtained a series of potential hydro power station
dispatching practice research schemes. We then go
further and analyze the practical feasibility of these
options in detail. This step is crucial because it helps
us identify those that can be implemented effectively
in the real world, as well as those that may be
theoretically feasible but difficult to apply in practice.
In order to more comprehensively verify the
effectiveness of different hydro power station
dispatching practice research schemes, we must com
pis multiple hydro power station dispatching practice
Table 1: Subject-related parameters of the study
Category Mean SD Analysis
rate
Compatibility
Energy
dis
p
atch
88.59 87.41 88.05 88.49
Stock
market
90.26 90.06 92.00 90.04
Weather
forecast
90.40 90.58 91.92 88.33
Data
analysis
92.76 84.53 89.23 90.32
Mean 86.32 89.32 88.37 83.86
X6 88.07 89.33 88.88 89.72
Test
Items
Test
value
p-
value
Test
analysis
Test rate
INCOFT 2025 - International Conference on Futuristic Technology
558

research schemes at different levels. These options
must be rigorously selected and compared to ensure
that they cover design strategies from basic to
advanced. In this way, we can create a more detailed
comparison framework, as shown in the table below
(Table 1), which details the features, advantages, and
performance of each design solution under different
conditions, so that we can make the most reasonable
choice accordingly.
3.3 Research on the Practice and
Stability of Hydro Power Station
Dispatching
The stability of hydro power station dispatching
practice research is the key element to ensure the
long-term effective operation of the system and
provide reliable services. A stable industrial hydro
power plant system is able to continuously deliver
high-quality search results in the face of different
search loads, changes in user behavior, and data
updates, without drastic performance degradation or
service interruption due to external changes.
Several aspects of the research on the impact of
stability on the dispatching practice of hydro power
stations include: the robustness of the system
architecture of the hydro power station dispatching
practice: a strong system architecture is the basis for
ensuring stability. This typically involves redundant
design, fault-tolerant mechanisms, and highly
available hardwired and softwoods resources to
prevent a single point of failure that could lead to the
collapse of the entire system. hydro power station
dispatching practices study the accuracy of data
processing: Industrial hydro power plant systems
need to process and analyze data accurately to ensure
the reliability of search results. This requires the
algorithm logic to be able to handle a variety of
boundary conditions and anomalies, and to maintain
consistency in the results when the data is updated or
the structure changes. hydro power station
dispatching practice studies the consistency of search
efficiency: the efficiency of the system should be
consistent when dealing with searches of various
scales. Whether it's a small amount of data searching
or a large batch of data processing, the system should
provide stable response times to avoid performance
degradation under high loads. hydro power station
dispatching practice research anti-interference
ability: a stable industrial hydro power station system
should be able to adapt to the influence of external
interference factors such as network fluctuations and
system load changes, and avoid service interruption
or failure. hydro power station dispatching practice
studies scalability and adaptability: With the increase
of resources and the development of technology, the
system should be able to flexibly expand and adapt to
new search needs and data types to ensure stable
service delivery.
Achieving the stability of an industrial hydro
power plant system usually requires the following
strategies: hydro power station dispatching practice
studies continuous performance monitoring: real-
time monitoring of system performance and user
behavior in order to identify potential problems in
time and make adjustments. hydro power station
dispatching practice research load balancing:
reasonable allocation of system resources and search
load can improve the pressure resistance and stability
of the system. hydro power station dispatching
practice studies regular maintenance and update:
regularly maintain and update the system, fix known
problems, and enhance system stability. hydro power
station dispatching practice research optimization
algorithm and data structure: optimize the underlying
algorithm and data structure to improve the
computing efficiency of the system and the ability to
stably handle a large number of concurrent searches.
hydro power station dispatching practice studies
develop detailed disaster recovery plans to ensure that
the system can recover quickly after a major failure.
Research on user feedback and system iteration of
hydro power station dispatching practice: Actively
collect user feedback, continuously iterate and update
the system, and improve stability and satisfaction.
Through these measures, the practical research on
hydro power station dispatching aims to create a
stable service platform that can not only adapt to the
actual needs but also respond quickly to future
changes. In order to verify the accuracy of the
artificial intelligence algorithm, the research scheme
of hydro power station dispatching practice is
compared with the breadth search algorithm, and the
hydro power station dispatching practice research
scheme is shown in Figure 2.
Figure 2: Practical research on hydro power station
dispatching with different algorithms
Practical Research on the Combination of Hydro Power Station Dispatching and Artificial Intelligence Algorithms
559

By examining the comparison of the data and
charts in Figure 2, we can clearly see that the AI
algorithm surpasses the breadth search algorithm in
the execution effect of hydro power station
dispatching practice research, and its error rate is
relatively low. This low error rate points to an
important conclusion, that is, the application of
artificial intelligence algorithms to the practical
research of hydro power station dispatching brings a
relatively stable and reliable performance. On the
contrary, although the breadth search algorithm has
its application in the practical research of hydro
power station scheduling, its results fluctuate greatly,
resulting in inconsistent overall performance. This
fluctuation may be due to the limitations and
challenges that breadth search algorithms may face
when dealing with complex and variable tasks in
industrial hydro power plants. In other words, the
breadth search algorithm shows an uneven effect in
the practical research of hydro power station
scheduling, which reduces its application value and
reliability in this isa to a certain extent. In conclusion,
the stability and low error rate of artificial intelligence
algorithms show their superiority in the field of hydro
power station dispatching practice, while the breadth
search algorithm shows limitations in such
applications. Therefore, when seeking a practical
research scheme for hydro power station dispatching
with high efficiency and stable performance, artificial
intelligence algorithm may be a more reasonable
choice.
Figure 3: Research on the practice of hydro power station
dispatching with artificial intelligence algorithm
Figure 3 shows that the artificial intelligence
algorithm is used to obtain better performance than
the breadth search algorithm in the practical study of
hydro power station scheduling. There may be several
key factors that make AI algorithms work well:
Introduction of adjustment coefficients: In industrial
hydro power plant process simulations, AI algorithms
may introduce adjustment coefficients to adjust
parameters in the simulation process in more detail.
These coefficients may be closely related to the
specific operating conditions or reactor design in the
lab, allowing the algorithm to more accurately reflect
and optimize real-world processes. Threshold setting
and scenario filtering: By setting thresholds for the
internet information obtained, the AI algorithm may
retain only those that meet the set criteria among
multiple candidates. This means that the algorithm is
able to automatically reject simulation results that
may be based on misinformation or unreliable data,
ensuring the quality of the optimization process.
Balance between exploration and utilization of swarm
algorithm: It maintains a good balance between
exploring and finding new solutions and optimizing
known solutions by exploiting them. This allows the
algorithm to avoid premature convergence to the local
optimal solution while maintaining efficient
optimization, and to explore a wider solution space as
shown in Figure II.
On the other hand, the poor performance of
breadth search algorithms in this context may be
related to some of their inherent limitations: Over
fitting: Decision trees may tend to be complex and, in
some cases, over fit the training data, resulting in
insufficient generalization capabilities for new data.
Selecting the local optimal solution: The decision tree
is split at each node only considering the local optimal
attributes, which may not capture the global optimal
parameter configuration of complex industrial hydro
power station processes.
Artificial intelligence algorithms search and
optimize multiple solutions in parallel, and
continuously use information sharing among group
members to guide the search process, so they can find
the global optimal or near-global optimal solutions
more than a single breadth search algorithm when
dealing with complex hydro power station
dispatching practice research scenarios. The
robustness and adaptability of this algorithm make it
an indispensable tool in fields such as bioengineering
and industrial process optimization.
INCOFT 2025 - International Conference on Futuristic Technology
560

Table 2: Rationalization and comparison of hydro power
station dispatching practices with different methods
Algori
thm
Size
of
sam
p
les
Me
an
R
Se 99%Con
fidence
interval
P-
val
ue
Accu
racy
Artific
ial
intelli
gence
algorit
hms
673 0.6
008
0.7
350
0.6722~
0.7294
0.7
362
1.45
13
Breadt
h
search
algorit
hm
679 0.7
985
3.7
818
0.6700~
0.8270
0.1
542
4.17
04
Figure 4: Comparative study of the research scheme of the
algorithm
It is evident from Figure 4 that the practical study
of hydro power station dispatching using artificial
intelligence algorithms far outperforms the design
using the breadth search algorithm. This significant
gap is mainly due to the fact that the artificial
intelligence algorithm has introduced a special
adjustment coefficient in the practical research
process of hydro power station scheduling. The
introduction of this coefficient enhances the
flexibility and adaptability of the algorithm, allowing
it to better adjust the strategy according to different
situations. In addition, AI algorithms set a specific
threshold for internet information processing.
Through this threshold setting, the algorithm can
effectively identify and exclude those hydro power
station dispatching practice research schemes that do
not meet the predetermined standards. This intelligent
screening mechanism makes the AI algorithm more
efficient when processing a large number of
candidates, ensuring that only the most suitable
solutions is selected to continue to participate in the
further design and evaluation phases. Combining
these two innovations, namely the introduction of
adjustment coefficients to improve the control ability
of the algorithm, and the setting of information
thresholds to accurately screen the design schemes
that meet the standards, the artificial intelligence
algorithm makes the practical research process of
hydro power station dispatching more efficient and
the output design scheme more high-quality. These
improvements finally form the core advantages of the
algorithm over the breadth search algorithm in the
practical research of hydro power station scheduling.
4 CONCLUSION
Aiming at the accuracy problem of hydro power
station dispatching practice, a new comprehensive
optimization scheme was proposed, which was based
on artificial intelligence algorithm and advanced
computer technology. Initially, the security of
information and the credibility of tampering with it
were ensured by the decentralized nature of AI
algorithms and their data consistency assurance.
Then, combined with computer technology, the
collected data is deeply analyzed and processed in
detail, so as to dig out the intrinsic attributes and
potential value of the data. This study also delves into
the key performance indicators required to ensure the
accuracy and credibility of hydro power station
dispatching practice research, and constructs a
comprehensive network information collection
platform, which plays a crucial role in ensuring the
accuracy of the research output. However, it is worth
noting that when applying artificial intelligence
algorithms, the selection of the evaluation system for
hydro power station dispatching practice research
must be cautious, so as to effectively explore and
utilize the advantages of artificial intelligence
algorithms and further improve the accuracy and
practical application value of research results.
ACKNOWLEDGEMENTS
Scientific Research Project of Lanzhou Resources &
Environment Voc-Tech University（X2023A-29）
Practical Research on the Combination of Hydro Power Station Dispatching and Artificial Intelligence Algorithms
561

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