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Готові списки джерел за темами / Optimization, Forecasting, Meta Learning, Model Selection / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Optimization, Forecasting, Meta Learning, Model Selection.

Статті в журналах з теми "Optimization, Forecasting, Meta Learning, Model Selection"

Автор: Grafiati

Опубліковано: 10 березня 2023

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1

Thi Kieu Tran, Trang, Taesam Lee, Ju-Young Shin, Jong-Suk Kim, and Mohamad Kamruzzaman. "Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization." Atmosphere 11, no. 5 (2020): 487. http://dx.doi.org/10.3390/atmos11050487.

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Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is

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2

Samuel, Omaji, Fahad A. Alzahrani, Raja Jalees Ul Hussen Khan, et al. "Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes." Entropy 22, no. 1 (2020): 68. http://dx.doi.org/10.3390/e22010068.

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Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electricity prices, and harmonizing energy sharing arrangement. The forecasting of the month ahead electrical loads provides the information required for the interchange of energy among power companies. For accurate load forecasting, this paper proposes a model for medium-term load forecasting that uses hourly electrical load and temperature data to predict month

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3

Ahmad, Waqas, Nasir Ayub, Tariq Ali, et al. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11 (2020): 2907. http://dx.doi.org/10.3390/en13112907.

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Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting.

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4

Ayub, Nasir, Muhammad Irfan, Muhammad Awais, et al. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19 (2020): 5193. http://dx.doi.org/10.3390/en13195193.

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Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A t

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5

Li, Yiyan, Si Zhang, Rongxing Hu, and Ning Lu. "A meta-learning based distribution system load forecasting model selection framework." Applied Energy 294 (July 2021): 116991. http://dx.doi.org/10.1016/j.apenergy.2021.116991.

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6

El-kenawy, El-Sayed M., Seyedali Mirjalili, Nima Khodadadi, et al. "Feature selection in wind speed forecasting systems based on meta-heuristic optimization." PLOS ONE 18, no. 2 (2023): e0278491. http://dx.doi.org/10.1371/journal.pone.0278491.

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Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm.

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7

Yang, Yi, Wei Liu, Tingting Zeng, Linhan Guo, Yong Qin, and Xue Wang. "An Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis." Scientific Programming 2022 (June 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/5415702.

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The purpose of spare parts management is to maximize the system’s availability and minimize the economic costs. The problem of cost availability trade-off leads to the problem of spare parts demand prediction. Accurate and reasonable spare parts demand forecasting can realize the balance between cost and availability. So, this paper focuses on spare parts management during the equipment normal operation phase and tries to forecast the demand of spare parts in a specific inspection and replacement cycle. Firstly, the equipment operation and support scenarios are analyzed to obtain the supportab

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8

Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.

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The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Ba

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9

Hafeez, Ghulam, Khurram Saleem Alimgeer, Zahid Wadud, et al. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid." Energies 13, no. 9 (2020): 2244. http://dx.doi.org/10.3390/en13092244.

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Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann

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10

Dokur, Emrah, Cihan Karakuzu, Uğur Yüzgeç, and Mehmet Kurban. "Using optimal choice of parameters for meta-extreme learning machine method in wind energy application." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (2021): 390–401. http://dx.doi.org/10.1108/compel-07-2020-0246.

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Purpose This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process. Design/methodology/approach The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtaine

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11

Zhao, Yu Hong, Xue Cheng Zhao, and Wei Cheng. "The Application of Chaotic Particle Swarm Optimization Algorithm in Power System Load Forecasting." Advanced Materials Research 614-615 (December 2012): 866–69. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.866.

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The support vector machine (SVM) has been successfully applied in the short-term load forecasting area, but its learning and generalization ability depends on a proper setting of its parameters. In order to improve forecasting accuracy, aiming at the disadvantages like man-made blindness in the parameters selection of SVM, In this paper, the chaos theory was applied to the PSO (particles swarm optimization) algorithm in order to cope with the problems such as low search speed and local optimization. Finally, we used it to optimize the support vector machines of short-term load forecasting mode

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12

Chen, Jun, Chenyang Zhao, Kaikai Liu, Jingjing Liang, Huan Wu, and Shiyan Xu. "Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models." Computational Intelligence and Neuroscience 2021 (September 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/2993870.

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Today, the global exchange market has been the world’s largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization alg

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13

Sun, Sizhou, Jingqi Fu, Feng Zhu, and Dajun Du. "A hybrid structure of an extreme learning machine combined with feature selection, signal decomposition and parameter optimization for short-term wind speed forecasting." Transactions of the Institute of Measurement and Control 42, no. 1 (2018): 3–21. http://dx.doi.org/10.1177/0142331218771141.

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Influenced by various environmental and meteorological factors, wind speed presents stochastic and unstable characteristics, which makes it difficult to forecast. To enhance the forecasting accuracy, this study contributes to short-term multi-step hybrid wind speed forecasting (WSF) models using wavelet packet decomposition (WPD), feature selection (FS) and an extreme learning machine (ELM) with parameter optimization. In the model, the WPD technique is applied to decompose the empirical wind speed data into different, relatively stable components to reduce the influence of the unstable charac

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14

Quan, Jicheng, and Li Shang. "Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization." Mathematical Problems in Engineering 2020 (November 27, 2020): 1–13. http://dx.doi.org/10.1155/2020/7212368.

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Short-term wind speed forecasting is crucial to the utilization of wind energy, and it has been employed widely in turbine regulation, electricity market clearing, and preload sharing. However, the wind speed has inherent fluctuation, and accurate wind speed prediction is challenging. This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm, ensemble learning, and an efficient artificial neural network. Variational mode decomposition (VMD) is

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15

Kim, Gi Yong, Doo Sol Han, and Zoonky Lee. "Solar Panel Tilt Angle Optimization Using Machine Learning Model: A Case Study of Daegu City, South Korea." Energies 13, no. 3 (2020): 529. http://dx.doi.org/10.3390/en13030529.

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Finding optimal panel tilt angle of photovoltaic system is an important matter as it would convert the amount of sunlight received into energy efficiently. Numbers of studies used various research methods to find tilt angle that maximizes the amount of radiation received by the solar panel. However, recent studies have found that conversion efficiency is not solely dependent on the amount of radiation received. In this study, we propose a solar panel tilt angle optimization model using machine learning algorithms. Rather than trying to maximize the received radiation, the objective is to find

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16

Jasiński, Tomasz, and Agnieszka Ścianowska. "Security assessment and optimization of energy supply (neural networks approach)." Oeconomia Copernicana 6, no. 2 (2015): 129. http://dx.doi.org/10.12775/oec.2015.016.

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The question of energy supply continuity is essential from the perspective of the functioning of society and the economy today. The study describes modern methods of forecasting emergency situations using Artificial Intelligence (AI) tools, especially neural networks. It examines the structure of a properly functioning model in the areas of input data selection, network topology and learning algorithms, analyzes the functioning of an energy market built on the basis of a reserve market, and discusses the possibilities of economic optimization of such a model, including the question of safety.

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17

Gollagi, Shantappa G., Jeneetha Jebanazer J, and Sridevi Sakhamuri. "Software Defects Prediction Model with Self Improved Optimization." International Journal of Software Innovation 10, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijsi.309735.

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Software systems have significantly grown and increased its complexity to unprecedented levels. Because of these characteristics, preventing software faults is extremely difficult. Therefore, automatic forecasting of errors is required, and it might assist developers deploy with limited resources more efficiently. Different methods on identifying and correcting these flaws at low cost were offered, which, significantly improves the effectiveness of the techniques. This work includes 4 steps to offer a new SDP model. The input data is preprocessed and from that, the “statistical features, raw f

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18

Wu, Kaitong, Xiangang Peng, Zilu Li, et al. "A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection." Energies 15, no. 15 (2022): 5410. http://dx.doi.org/10.3390/en15155410.

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High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant fe

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19

Deepika, Nalabala, and Mundukur Nirupamabhat. "An Optimized Machine Learning Model for Stock Trend Anticipation." Ingénierie des systèmes d information 25, no. 6 (2020): 783–92. http://dx.doi.org/10.18280/isi.250608.

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Security market is an economical-volatile in nature as it is driven by not only based on historical prices various unpredictable external factors like financial news, changes in socio-political issues and natural calamities happened in real world; hence its forecasting is a challenging task for traders. To gain profits and to overcome any crisis in financial market, it is essential to have a very accurate calculation of future trends by for the investors. The trend prediction results can be used as recommendations for investors as to whether they should buy or sell. Feature selection, dimensio

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20

Massaoudi, Mohamed, Ines Chihi, Lilia Sidhom, Mohamed Trabelsi, Shady S. Refaat, and Fakhreddine S. Oueslati. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements." Energies 14, no. 13 (2021): 3992. http://dx.doi.org/10.3390/en14133992.

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Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a

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21

Leka, Habte Lejebo, Zhang Fengli, Ayantu Tesfaye Kenea, Negalign Wake Hundera, Tewodros Gizaw Tohye, and Abebe Tamrat Tegene. "PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction." Symmetry 15, no. 3 (2023): 613. http://dx.doi.org/10.3390/sym15030613.

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To meet the increasing demand for its services, a cloud system should make optimum use of its available resources. Additionally, the high and low oscillations in cloud workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning workload forecasting approach uses base models and the PSO-optimized weights of their network inputs. The proposed model employs a blended ensemble learning strategy to merge three recurrent neural networks (RNNs), followed by a dense neural network layer. The CPU utilizat

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22

Motwakel, Abdelwahed, Eatedal Alabdulkreem, Abdulbaset Gaddah, et al. "Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids." Sustainability 15, no. 2 (2023): 1524. http://dx.doi.org/10.3390/su15021524.

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Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. T

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23

Kanavos, Andreas, Maria Trigka, Elias Dritsas, Gerasimos Vonitsanos, and Phivos Mylonas. "A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data." Electronics 10, no. 16 (2021): 1872. http://dx.doi.org/10.3390/electronics10161872.

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In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) ne

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24

Padhi, Dushmanta Kumar, Neelamadhab Padhy, Akash Kumar Bhoi, Jana Shafi, and Seid Hassen Yesuf. "An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–18. http://dx.doi.org/10.1155/2022/7588303.

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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio con

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25

Calvo-Pardo, Hector F., Tullio Mancini, and Jose Olmo. "Neural Network Models for Empirical Finance." Journal of Risk and Financial Management 13, no. 11 (2020): 265. http://dx.doi.org/10.3390/jrfm13110265.

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This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review al

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26

Liu, Mingping, Xihao Sun, Qingnian Wang, and Suhui Deng. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model." Energies 15, no. 19 (2022): 7170. http://dx.doi.org/10.3390/en15197170.

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Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a s

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27

Munsarif, Muhammad, Muhammad Sam’an, and Safuan Safuan. "Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3483–89. http://dx.doi.org/10.11591/eei.v11i6.3927.

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Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and deci

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28

Manliura Datilo, Philemon, Zuhaimy Ismail, and Jayeola Dare. "A Review of Epidemic Forecasting Using Artificial Neural Networks." International Journal of Epidemiologic Research 6, no. 3 (2019): 132–43. http://dx.doi.org/10.15171/ijer.2019.24.

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Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It

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29

Wu, Yuan-Kang, Cheng-Liang Huang, Quoc-Thang Phan, and Yuan-Yao Li. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints." Energies 15, no. 9 (2022): 3320. http://dx.doi.org/10.3390/en15093320.

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Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, vari

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30

Hu, Zhongyi, Yukun Bao, and Tao Xiong. "Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression." Applied Soft Computing 25 (December 2014): 15–25. http://dx.doi.org/10.1016/j.asoc.2014.09.007.

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31

Yaprakdal, Fatma. "An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods." Energies 16, no. 1 (2022): 57. http://dx.doi.org/10.3390/en16010057.

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Анотація:

The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. A

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32

Masrom, Suraya, Rahayu Abdul Rahman, Masurah Mohamad, Abdullah Sani Abd Rahman, and Norhayati Baharun. "Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1153. http://dx.doi.org/10.11591/ijai.v11.i3.pp1153-1163.

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This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This p

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Mahdavian, Amirsaman, Alireza Shojaei, Milad Salem, Haluk Laman, Jiann-Shiun Yuan, and Amr Oloufa. "Automated Machine Learning Pipeline for Traffic Count Prediction." Modelling 2, no. 4 (2021): 482–513. http://dx.doi.org/10.3390/modelling2040026.

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Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literature, studies using broad data sets and inclusive predictors have been inadequate; such works have not incorporated a comprehensive set of linear and nonlinear algorithms utilizing a robust cross-validation approach. The proposed model pipeline introduced in this study automatically identifies the most appropriate feature-selection method and modelin

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Nguyen, P., M. Hilario, and A. Kalousis. "Using Meta-mining to Support Data Mining Workflow Planning and Optimization." Journal of Artificial Intelligence Research 51 (November 29, 2014): 605–44. http://dx.doi.org/10.1613/jair.4377.

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Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descri

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Bouktif, Salah, Ali Fiaz, Ali Ouni, and Mohamed Adel Serhani. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting." Energies 13, no. 2 (2020): 391. http://dx.doi.org/10.3390/en13020391.

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Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As the volume and variety of energy data provided by building automation systems, smart meters, and other sources are continuously increasing, long short-term memory (LSTM) deep learning models have become an attractive approach for energy load forecasting. These models are characterized by their capabilities of learning long-term dependencies in coll

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36

Yan, Guangxi, Yu Bai, Chengqing Yu, and Chengming Yu. "A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework." Machines 10, no. 9 (2022): 759. http://dx.doi.org/10.3390/machines10090759.

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Recently, with the increasing scale of the volume of freight transport and the number of passengers, the study of railway vehicle fault diagnosis and condition management is becoming more significant than ever. The axle temperature plays a significant role in the locomotive operating condition assessment that sudden temperature changes may lead to potential accidents. To realize accurate real-time condition monitoring and fault diagnosis, a new multi-data-driven model based on reinforcement learning and deep learning is proposed in this paper. The whole modeling process contains three steps: I

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Lu, Feng Yi, Shuang Wang, Ge Ning Xu, and Qi Song Qi. "Research on Parameter Optimization Method of v-SVRM Forecasting Model for Crane Load Spectrum." Advanced Materials Research 1078 (December 2014): 191–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.191.

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Precise load spectrum of crane is essential to its fatigue analysis and life assessment. The v-SVRM (v-support vector regression machine) correctly established is key to an undistorted load spectrum. Due to computational complexity, low accuracy, poor stability of the conventional model parameter selection method with v-SVRM, a fruit fly optimization algorithm with the characteristics of easy adjustment and high precision is applied. In order to make three kind parameters synchronously optimizing search, the fruit fly algorithm is improved in consideration of parameters characteristic of the c

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Ai, Ping, Yanhong Song, Chuansheng Xiong, Binbin Chen, and Zhaoxin Yue. "A novel medium- and long-term runoff combined forecasting model based on different lag periods." Journal of Hydroinformatics 24, no. 2 (2022): 367–87. http://dx.doi.org/10.2166/hydro.2022.116.

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Abstract The accuracy of medium- and long-term runoff forecasting plays a signiﬁcant role in several applications involving the management of hydrological resources, such as power generation, water supply and ﬂood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In

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Tuerxun, Wumaier, Chang Xu, Hongyu Guo, Lei Guo, Namei Zeng, and Yansong Gao. "A Wind Power Forecasting Model Using LSTM Optimized by the Modified Bald Eagle Search Algorithm." Energies 15, no. 6 (2022): 2031. http://dx.doi.org/10.3390/en15062031.

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High-precision forecasting of short-term wind power (WP) is integral for wind farms, the safe dispatch of power systems, and the stable operation of the power grid. Currently, the data related to the operation and maintenance of wind farms mainly comes from the Supervisory Control and Data Acquisition (SCADA) systems, with certain information about the operating characteristics of wind turbines being readable in the SCADA data. In short-term WP forecasting, Long Short-Term Memory (LSTM) is a commonly used in-depth learning method. In the present study, an optimized LSTM based on the modified b

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Mogbojuri, A. O., and O. A. Olanrewaju. "Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review." Nigerian Journal of Technology 41, no. 5 (2022): 862–69. http://dx.doi.org/10.4314/njt.v41i5.6.

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Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio se

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Almulihi, Ahmed, Hager Saleh, Ali Mohamed Hussien, et al. "Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction." Diagnostics 12, no. 12 (2022): 3215. http://dx.doi.org/10.3390/diagnostics12123215.

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Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of h

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Rath, Smita, Binod Kumar Sahu, and Manoj Ranjan Nayak. "Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction." International Journal of Intelligent Computing and Cybernetics 12, no. 2 (2019): 175–93. http://dx.doi.org/10.1108/ijicc-10-2018-0145.

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Purpose Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices. Design/methodology/approach A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effect

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Ang, Yik Kang, Amin Talei, Izni Zahidi, and Ali Rashidi. "Past, Present, and Future of Using Neuro-Fuzzy Systems for Hydrological Modeling and Forecasting." Hydrology 10, no. 2 (2023): 36. http://dx.doi.org/10.3390/hydrology10020036.

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Neuro-fuzzy systems (NFS), as part of artificial intelligence (AI) techniques, have become popular in modeling and forecasting applications in many fields in the past few decades. NFS are powerful tools for mapping complex associations between inputs and outputs by learning from available data. Therefore, such techniques have been found helpful for hydrological modeling and forecasting, including rainfall–runoff modeling, flood forecasting, rainfall prediction, water quality modeling, etc. Their performance has been compared with physically based models and data-driven techniques (e.g., regres

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Kumar, Akash, Bing Yan, and Ace Bilton. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction." Energies 15, no. 18 (2022): 6721. http://dx.doi.org/10.3390/en15186721.

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Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours,” raises the energy cost substantially. In this paper, an ANN model with dy

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45

Zirngibl, Christoph, Fabian Dworschak, Benjamin Schleich, and Sandro Wartzack. "Application of reinforcement learning for the optimization of clinch joint characteristics." Production Engineering 16, no. 2-3 (2021): 315–25. http://dx.doi.org/10.1007/s11740-021-01098-4.

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AbstractDue to increasing challenges in the area of lightweight design, the demand for time- and cost-effective joining technologies is steadily rising. For this, cold-forming processes provide a fast and environmentally friendly alternative to common joining methods, such as welding. However, to ensure a sufficient applicability in combination with a high reliability of the joint connection, not only the selection of a best-fitting process, but also the suitable dimensioning of the individual joint is crucial. Therefore, few studies already investigated the systematic analysis of clinched joi

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Izidio, Diogo M. F., Paulo S. G. de Mattos Neto, Luciano Barbosa, João F. L. de Oliveira, Manoel Henrique da Nóbrega Marinho, and Guilherme Ferretti Rissi. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters." Energies 14, no. 7 (2021): 1794. http://dx.doi.org/10.3390/en14071794.

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The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results

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47

Zamee, Muhammad Ahsan, and Dongjun Won. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction." Energies 13, no. 23 (2020): 6405. http://dx.doi.org/10.3390/en13236405.

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Анотація:

A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s

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Aghelpour, Pouya, Babak Mohammadi, Seyed Mostafa Biazar, Ozgur Kisi, and Zohreh Sourmirinezhad. "A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods." ISPRS International Journal of Geo-Information 9, no. 12 (2020): 701. http://dx.doi.org/10.3390/ijgi9120701.

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The met

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Kumar, Akshi, Arunima Jaiswal, Shikhar Garg, Shobhit Verma, and Siddhant Kumar. "Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets." International Journal of Information Retrieval Research 9, no. 1 (2019): 1–15. http://dx.doi.org/10.4018/ijirr.2019010101.

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Selecting the optimal set of features to determine sentiment in online textual content is imperative for superior classification results. Optimal feature selection is computationally hard task and fosters the need for devising novel techniques to improve the classifier performance. In this work, the binary adaptation of cuckoo search (nature inspired, meta-heuristic algorithm) known as the Binary Cuckoo Search is proposed for the optimum feature selection for a sentiment analysis of textual online content. The baseline supervised learning techniques such as SVM, etc., have been firstly impleme

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Bakurova, Anna, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, and Elina Tereschenko. "NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE." Innovative Technologies and Scientific Solutions for Industries, no. 1 (15) (March 31, 2021): 14–22. http://dx.doi.org/10.30837/itssi.2021.15.014.

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The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the power system of a metallurgical enterprise and its experimental testing on the data available for research of PJSC "Dneprospetsstal". The following tasks have been solved: analysis of the time series of power consumption; building a model with the help of which data on electricity consumption for a h

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