A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting

Kavousi‐Fard, Abdollah; Akbari-Zadeh, Mohammad-Reza

doi:10.1080/0952813x.2013.813976

Cited by 114 publications

(35 citation statements)

References 25 publications

(18 reference statements)

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“…At present, there are many methods for predicting ultrashort-term renewable energy and load, mostly using Kalman filtering [21][22][23], artificial neural networks [24], time series [25], and the gray theory method [26,27]. Compared to other prediction methods, the gray prediction method does not need to determine whether the renewable energy and the thermal power fluctuations follow a normal distribution and does not require large sample statistics.…”

Section: Prediction Modelmentioning

confidence: 99%

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

Luo

Zhu

et al. 2020

Processes

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With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

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Section: Prediction Modelmentioning

confidence: 99%

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

Luo

Zhu

et al. 2020

Processes

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“…Combination of NNs and ARIMA [14] this work proposed a hybrid model combining NNs and ARIMA, which is capable of exploiting the strengths of traditional time series approaches and artificial neural networks.…”

Section: Markov Chain Modelmentioning

confidence: 99%

When Traffic Flow Prediction and Wireless Big Data Analytics Meet

et al. 2019

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Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. This article first gives a brief introduction to the transportation data, and surveys the state-ofthe-art prediction methods. Then, we verify whether or not the prediction performance is able to be improved by fitting actual data to optimize the parameters of the prediction model which is used to predict the traffic flow. Such verification is conducted by comparing the optimized time series prediction model with the normal time series prediction model. This means that in the era of big data, accurate use of the data becomes the focus of studying the traffic flow prediction to solve the congestion problem. Finally, experimental results of a case study are provided to verify the existence of such performance improvement, while the research challenges of this data-analytics-based prediction are presented and discussed.

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“…Some researchers combine different models, such as EMD-BPN [13,14], to forecast the volume of the passengers. As for the last one, with the help of the cellular automaton model, the volume can be forecasted.…”

Section: Passenger Data Monitoring and Forecastingmentioning

confidence: 99%

Congestion Risk Evaluation and Precaution of Passenger Flow in Metro Stations

Lu¹,

Han²

2016

TOCIEJ

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Abstract:Given the security problems of the urban rail transportation, this paper proposes a methodology for congestion risk evaluation and precaution in order to guarantee the safety of passengers and improve the operation efficiency in metro stations. To begin with, we selected the retention rate of the platform, the waiting rate of the ticket gate and the transfer efficiency as the indexes for the station congestion precaution system and created models to calculate the threshold respectively. By reasonably combining these three indexes, a congestion warning level function was set up to evaluate the congestion risk and determine the warning level. The characteristics of the proposed model are its quick decision results and ease of handling. With the determined warning level, some strategies are suggested to relieve the pressure. Finally, a practical application in Beijing's GuoMao metro station was performed to verify the feasibility and practicability of the proposed congestion precaution model.

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A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting

Cited by 114 publications

References 25 publications

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

When Traffic Flow Prediction and Wireless Big Data Analytics Meet

Congestion Risk Evaluation and Precaution of Passenger Flow in Metro Stations

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