Special Issue "Hybrid Intelligent Methods for Forecasting in Resources and Energy Field"

Submission Deadline: 31 December 2021 (closed)
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
  • Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches
  • 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… More
  •   Views:307       Downloads:123        Download PDF


  • Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique
  • 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… More
  •   Views:475       Downloads:160        Download PDF

  • Comparative Study on Deformation Prediction Models of Wuqiangxi Concrete Gravity Dam Based on Monitoring Data
  • 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 J23 (on dam section ), J33 (on dam section… More
  •   Views:547       Downloads:466        Download PDF

  • A Novel Indoor Positioning Framework
  • 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… More
  •   Views:499       Downloads:411        Download PDF


  • Quantification of Urban Sprawl for Past-To-Future in Abha City, Saudi Arabia
  • 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… More
  •   Views:1142       Downloads:827       Cited by:2        Download PDF

  • Code Transform Model Producing High-Performance Program
  • 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… More
  •   Views:1150       Downloads:888        Download PDF


  • Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm
  • 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… More
  •   Views:915       Downloads:652        Download PDF

  • Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN
  • 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… More
  •   Views:1273       Downloads:764       Cited by:1        Download PDF