Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

Pourdaryaei, Alireza; Mokhlis, Hazlie; Illias, Hazlee Azil; Kaboli, S. Hr. Aghay; Ahmad, Shameem

doi:10.1109/access.2019.2922420

Cited by 60 publications

(43 citation statements)

References 42 publications

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“…Using the results of this article in modeling urban transportation networks by means of bi-level programming models and solving the models via meta-heuristic algorithms like genetic algorithm, backtracking search algorithm, and particle swarm optimization, and then comparing the results to those of the present study. Considering solution approaches, interested readers may refer to [54][55][56]. 5.…”

Section: Resultsmentioning

confidence: 99%

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

2020

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Recently, the researchers in the field of transportation network planning have become increasingly interested in network reliability, publishing research works focused on the calculation of various types of network reliability. Calculation of network reliability has led the transportation network optimizers toward new approaches where the maximization of reliability has been considered as an objective. Travel time reliability is among the most important reliabilities investigated when analyzing urban transportation networks, with various approaches based on different assumptions proposed for calculating it. In the present research, the uncertainty associated with the demand for travel and the flows passing across links and also the correlations among the links comprising a route were considered to calculate the travel time for each of the network links. Moreover, it was shown that this process follows shifted log-normal distribution. These calculations are expected to serve as a basis for the employment of travel time reliability within the modeling of an transportation system, so as to increase the accuracy and reliability of the simulations. Finally, in order to validate, an urban network with 12 nodes, 21 links, and 4 origin-destination pairs was subjected to the travel time reliability assessment by calculating the travel time over all forming links.

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Section: Resultsmentioning

confidence: 99%

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

2020

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“…Recently, a multi-objective feature selection technique that comprises the multi-objective binary BSA (MOBBSA) as a competent EA and an adaptive neuro-fuzzy inference system (ANFIS) approach for forecasting short-term electricity price were proposed [98]. The suggested hybrid method is called MOBBSA.…”

Section: Mobbsamentioning

confidence: 99%

“…Therefore, BSAMO is considered as a possible alternative to minimising multi-objective optimisation problems. Concurrently, another MOBBSA was used to forecast the price of electricity [98]. This algorithm was applied to real-world electricity sales datasets.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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“…The artificial intelligence (AI) based techniques have been considered a superior solution for the computation of AC induced voltages [17,20]. AI based approaches often warranty a sufficient degree of prediction accuracy for complex system modeling [21].…”

Section: Literate Reviewmentioning

confidence: 99%

Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm

et al. 2019

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The electromagnetic interference (EMI) generated by high voltage power systems can cause a serious problem for nearby electrically conductive structures, such as railroads, communication lines, or pipelines, that would place a system’s integrity and the operational safety of the structure at high level of risk. According to the IEEE standard-80, by implementing a well-designed mitigation system, the induced voltage on neighboring electrically conductive structure can reach a harmless level. The mitigation system can enhance the overall integrity of pipelines and provide higher operation safety for personal during working on the exposed parts of metallic pipelines or conductive appurtenances. An accurate prediction about the level of induced voltage is absolutely necessary to design a suitable mitigation system for metallic pipelines. Thus, in this work a hybrid prediction methodology composed of an adaptive neuro-fuzzy inference system (ANFIS) and a backtracking search algorithm (BSA) is developed to accurately predict the electromagnetic inference’s effects on metallic pipelines with shared right-of-way (RoW) and high voltage overhead lines (OHLs). Through the combination of BSA as a robust and efficient optimization algorithm in the learning process of an ANFIS approach, a hybrid data mining algorithm has been developed to predict the induced voltage on mitigated and unmitigated pipelines more accurately and reliably. The simulation results are validated by data sets observed from the Current Distribution, Electromagnetic Interference, Grounding and Soil Structure Analysis (CDEGS) software. From the simulation results it was confirmed that the proposed hybrid method is effective in accurately predicting the induced voltage on pipelines with changing system parameters. Furthermore, to evaluate the precision and applicability of the developed approach in this paper, its estimates are compared with the results obtained from an artificial neural network (ANN), a support vector regression (SVR) and an ANFIS optimized by other well-known optimization algorithms. The obtained results indicate higher accuracy of the developed hybrid method over other artificial intelligence based approaches.

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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

Cited by 60 publications

References 42 publications

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm

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