Guest Editors
Prof. Dr. Wei-Chiang Hong, Oriental Institute of Technology, Taiwan
Dr. Yi Liang, Hebei Geo University, China
Summary
Precise resources and energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in energy system planning, maintenance, operation, security, and so on. In the past decades, many resources and energy forecasting models have been continuously proposed to increase the forecasting accuracy, especially intelligence models (e.g., artificial neural networks, support vector regression, evolutionary computation models, etc.). Meanwhile, due to the great development of optimization methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, etc.), many novel hybrid methods combined with the above-mentioned intelligent-optimization-based methods have also been proposed to achieve satisfactory forecasting accuracy levels. It is worthwhile to explore the tendency and development of intelligent-optimization-based hybrid methodologies and to enrich their practical performances, particularly for resources and energy forecasting.
Potential topics include but are not limited to the following:
• hybrid methods
• artificial neural networks methods
• support vector regression methods
• evolutionary computation methods
• quadratic programming methods
• resources forecasting
• energy forecasting
Published Papers
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Open Access
EDITORIAL
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Open Access
ARTICLE
Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network
Tingshun Li, Jiaohui Xu, Zesan Liu, Dadi Wang, Wen Tan
Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 767-782, 2023, DOI:10.32604/cmes.2022.020702
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where
this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades
of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes
a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data
Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is,
they are labeled, normalized, and balanced. The…
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Open Access
ARTICLE
Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches
Bao Rong Chang, Hsiu-Fen Tsai, Yu-Chieh Lin
Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that
will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data
retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low
throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data
searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up
data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives
prioritized…
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Open Access
ARTICLE
Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique
Yang Yang, Pengfei Zheng, Fanru Zeng, Peng Xin, Guoxi He, Kexi Liao
Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 267-291, 2023, DOI:10.32604/cmes.2022.020220
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent
pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal
corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of
pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm
to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into
training and test set with a 9:1 ratio. The…
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Open Access
ARTICLE
An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems
Yadian Geng, Junqing Li
Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 241-266, 2023, DOI:10.32604/cmes.2022.020307
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we
investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously
considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution
is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of
the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take
more advantage of non-dominated solutions. Furthermore, a local search heuristic based on…
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Open Access
ARTICLE
Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy
Huiying Wu, Meihua Zou, Ye Ke, Wenqi Ou, Yonghong Li, Minquan Ye
Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 739-761, 2022, DOI:10.32604/cmes.2022.019714
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively, which has important practical significance for the further development of the power substation project. To ensure accuracy and real-time evaluation, this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory (LSTM) optimized by a Sperm Whale Algorithm (SWA). Firstly, under the background of considering the development of new energy, the influencing factors of power substation project implementation effect are analyzed from three aspects of technology, economy and society. Moreover, an…
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Open Access
ARTICLE
Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data
Songlin Yang, Xingjin Han, Chufeng Kuang, Weihua Fang, Jianfei Zhang, Tiantang Yu
Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 49-72, 2022, DOI:10.32604/cmes.2022.018325
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract The deformation prediction models of Wuqiangxi concrete gravity dam are developed, including two statistical models and a deep learning model. In the statistical models, the reliable monitoring data are firstly determined with Lahitte criterion; then, the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data, and the factors of water pressure, temperature and time effect are considered in the models; finally, according to the monitoring data from 2006 to 2020 of five typical measuring points including J
23 (on dam section ), J
33 (on dam section…
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Open Access
ARTICLE
A Novel Indoor Positioning Framework
Ming-Chih Chen, Yin-Ting Cheng, Ru-Wei Chen
Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1459-1477, 2022, DOI:10.32604/cmes.2022.015636
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Current positioning systems are primarily based on the Global Positioning System (GPS). Although the GPS is accurate within 10 m, it is mainly used for outdoor positioning services (Location-Based Service; LBS). However, since satellite signals cannot penetrate buildings, indoor positioning has always been a blind spot for satellite signals. As indoor positioning applications are extensive with high commercial values, they have created a competitive niche in the market. Existing indoor positioning technologies are unable to achieve less than 10 cm accuracy except for the Ultra Wide Band (UWB) technology. On the other hand, the Bluetooth protocol achieves an accuracy of…
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Open Access
ARTICLE
Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm
Qian Wang, Xiaolong Yang, Di Pu, Yingying Fan
Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 269-286, 2022, DOI:10.32604/cmes.2022.016437
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model was used to predict the…
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Open Access
ARTICLE
Quantification of Urban Sprawl for Past-To-Future in Abha City, Saudi Arabia
Saeed AlQadhi, Javed Mallick, Swapan Talukdar, Ahmed Ali Bindajam, Ahmed Ali A. Shohan, Shahfahad
Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 755-786, 2021, DOI:10.32604/cmes.2021.016640
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Given that many cities in Saudi Arabia have been observing rapid urbanization since the 1990s, scarce studies on the
spatial pattern of urban expansion in Saudi Arabia have been conducted. Therefore, the present study investigates
the evidence of land use and land cover (LULC) dynamics and urban sprawl in Abha City of Saudi Arabia, which
has been experiencing rapid urbanization, from the past to the future using novel and sophisticated methods. The
SVM classifier was used in this study to classify the LULC maps for 1990, 2000, and 2018. The LULC dynamics
between 1990–2000, 2000–2018, and 1990–2018 have been analyzed…
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Open Access
ARTICLE
Code Transform Model Producing High-Performance Program
Bao Rong Chang, Hsiu-Fen Tsai, Po-Wen Su
Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 253-277, 2021, DOI:10.32604/cmes.2021.015673
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract This paper introduces a novel transform method to produce the newly generated programs through code transform
model called the second generation of Generative Pre-trained Transformer (GPT-2) reasonably, improving the
program execution performance significantly. Besides, a theoretical estimation in statistics has given the minimum
number of generated programs as required, which guarantees to find the best one within them. The proposed
approach can help the voice assistant machine resolve the problem of inefficient execution of application code. In
addition to GPT-2, this study develops the variational Simhash algorithm to check the code similarity between
sample program and newly generated program, and…
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Open Access
ARTICLE
Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model
Liping Yan, Wei-Chiang Hong
Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1069-1102, 2021, DOI:10.32604/cmes.2021.016499
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road. In order to obtain the scientific
and real-time forecasting result, this paper constructs a novel hybrid intelligent model based on improved cloud
model combined with GRA-TOPSIS and MBA-WLSSVM. Firstly, the factors influencing investment risk of wind
energy along the Belt and Road are identified from three dimensions: endogenous risk, exogenous risk and process
risk. Through the fuzzy threshold method, the final input index system is selected. Secondly, the risk evaluation
method…
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Open Access
ARTICLE
Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm
Ming-Chih Chen, Yin-Ting Cheng, Ru-Wei Chen
Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1103-1119, 2021, DOI:10.32604/cmes.2021.015589
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract This work presents a fall detection system based on artificial intelligence. The system incorporates miniature wearable devices for fall detection. Fall detection is achieved by integrating a three-axis gyroscope and a three-axis accelerometer. The system gathers the differential data collected by the gyroscope and accelerometer, applies artificial intelligence algorithms for model training and constructs an effective model for fall detection. To provide easy wearing and effective position detection, it is designed as a small device attached to the user’s waist. Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%, demonstrating the effectiveness…
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Open Access
ARTICLE
Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN
Huizhi Gou, Yuncai Ning
Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922
（This article belongs to this Special Issue:
Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning
and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy
problems. To address this research objective, this paper proposes a prediction model based on kernel principal
component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks
(DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant
input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting
method of KPCA-MCS-DCNN is established. In…
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