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

Submission Deadline: 31 December 2021 (closed)
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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
  • 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:72       Downloads:49        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:153       Downloads:162        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:813       Downloads:542        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:818       Downloads:658        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:561       Downloads:413        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:917       Downloads:563        Download PDF