Prediction of transportation energy demand by novel hybrid meta-heuristic ANN

Sahraei, Mohammad Ali; Çodur, Merve Kayacı

doi:10.1016/j.energy.2022.123735

Cited by 41 publications

(9 citation statements)

References 64 publications

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“…As a result of the problems associated with the use of classical methods mentioned earlier, the use of heuristic and metaheuristic optimisation algorithms is becoming more and more common, which are used, among others, in [242][243][244][245][246]. These methods owe their popularity, among others, to their universality, flexibility, simplicity and relatively high effectiveness [247,248]. The disadvantages include the fact that the obtained results with a certain probability allow us to claim that the found optimum is global and the relatively long computation time.…”

Section: Literature Review Of the Application Of Optimization Methods...mentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Literature Review Of the Application Of Optimization Methods...mentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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“…The performance of ML algorithms in the energy demand problem was estimated using powerful validation techniques. Five validation methods, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R 2 ), were employed to evaluate the models [10].…”

Section: Performance Metricsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) have garnered significant interest in energy planning due to their ability to handle complex nonlinear relationships between input and output data [5]. ANNs have been applied in various energy forecasting applications, including gas consumption [6], energy demand [7], electricity consumption [8], transportation energy demand [9][10][11], energy source analysis [12], and energy dependency [7]. Apart from ANNs, other prediction methods have emerged, such as fuzzy logic, adaptive network-based fuzzy inference systems (ANFIS), and general machine learning algorithms [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand

Kayacı Çodur

2023

Energies

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Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).

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“…Whereas prior research investigated the application of SVM, ARIMA, AVN, ANN, ANFIS, and other machine learning (ML) techniques in electric load forecasting, current work has demonstrated that combining diferent ML techniques can bring a lot of benefts over a stand-alone approach. Because most ML methods are designed to handle a single job or dataset, combining diferent ML methods can greatly enhance the overall result by helping each other to tune, extend, or adapt to unfamiliar tasks [42]. Te aptitude to overcome specifc algorithm restrictions without sacrifcing efciency contributes to hybrid techniques outperforming stand-alone alternatives [2].…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence

Oladipo

Sun

Adeleke

2023

International Transactions on Electrical Energy Systems

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Future energy planning relies on understanding how much energy is produced and consumed. In response, this study developed a multihybrid adaptive neuro-fuzzy inference system (ANFIS) for students’ residences, using the University of Johannesburg residence, South Africa as a case study. The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm’s convergence behaviour and aids both local and global search. Following that, the modified PSO (MPSO) is used to optimize the ANFIS parameters for the best model prediction. A comparative analysis is conducted between the MPSO, the original PSO, and six other hybrid models using a dataset division of 70% for training and 30% for testing. Performance evaluation was carried out using three well-known performance benchmarks: root mean square error (RMSE), mean absolute deviation (MAD), and coefficient of variation (RCoV). The experimental results show that the performance of the proposed MPSO-ANFIS outperformed other methods with the least values of the RMSE (1.8928 KWh), MAD (1.5051 KWh), and RCoV (0.1370), respectively. Furthermore, when compared to the PSO-ANFIS, the MPSO-ANFIS demonstrated improvements in RMSE, MAD, and RCoV with 1.58%, 2.11%, and 5.23%, respectively. Based on the results, it can be concluded that the MPSO-ANFIS provides better prediction accuracy which is vital for strategic energy planning.

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Prediction of transportation energy demand by novel hybrid meta-heuristic ANN

Cited by 41 publications

References 64 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand

An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence

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