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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 (May 10, 2020): 487. http://dx.doi.org/10.3390/atmos11050487.
Повний текст джерелаАнотація:
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 important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).
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Samuel, Omaji, Fahad A. Alzahrani, Raja Jalees Ul Hussen Khan, Hassan Farooq, Muhammad Shafiq, Muhammad Khalil Afzal, and Nadeem Javaid. "Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes." Entropy 22, no. 1 (January 4, 2020): 68. http://dx.doi.org/10.3390/e22010068.
Повний текст джерелаАнотація:
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 ahead hourly electrical loads. For data preprocessing, modified entropy mutual information-based feature selection is used. It eliminates the redundancy and irrelevancy of features from the data. We employ the conditional restricted Boltzmann machine (CRBM) for the load forecasting. A meta-heuristic optimization algorithm Jaya is used to improve the CRBM’s accuracy rate and convergence. In addition, the consumers’ dynamic consumption behaviors are also investigated using a discrete-time Markov chain and an adaptive k-means is used to group their behaviors into clusters. We evaluated the proposed model using GEFCom2012 US utility dataset. Simulation results confirm that the proposed model achieves better accuracy, fast convergence, and low execution time as compared to other existing models in the literature.
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Ahmad, Waqas, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, and Adam Glowacz. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11 (June 5, 2020): 2907. http://dx.doi.org/10.3390/en13112907.
Повний текст джерелаАнотація:
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. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.
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Ayub, Nasir, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, and Fazal Muhammad. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19 (October 5, 2020): 5193. http://dx.doi.org/10.3390/en13195193.
Повний текст джерелаАнотація:
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 three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.
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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.
Повний текст джерела
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El-kenawy, El-Sayed M., Seyedali Mirjalili, Nima Khodadadi, Abdelaziz A. Abdelhamid, Marwa M. Eid, M. El-Said, and Abdelhameed Ibrahim. "Feature selection in wind speed forecasting systems based on meta-heuristic optimization." PLOS ONE 18, no. 2 (February 7, 2023): e0278491. http://dx.doi.org/10.1371/journal.pone.0278491.
Повний текст джерелаАнотація:
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. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm’s stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum.
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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.
Повний текст джерелаАнотація:
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 supportability data related to spare parts requirements. Then, drawing on the idea of ensemble learning, a new feature selection method has been designed, which can overcome the limitations of a single feature selection method. In addition, an improved stacking model is proposed to predict the demand for spare parts. In the traditional stacking model, there are two levels of learning, base-learning, and meta-learning, in which the outputs of base learners are taken as the input of the meta learner. However, the proposed model brings the initial feature together with the output of the base learner layer as the input of the meta learner layer. And experiments have shown that the performance of the improved stacking model is better than the base learners and the traditional stacking model on the same data set.
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Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (August 18, 2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.
Повний текст джерелаАнотація:
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-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
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Hafeez, Ghulam, Khurram Saleem Alimgeer, Zahid Wadud, Zeeshan Shafiq, Mohammad Usman Ali Khan, Imran Khan, Farrukh Aslam Khan, and Abdelouahid Derhab. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid." Energies 13, no. 9 (May 3, 2020): 2244. http://dx.doi.org/10.3390/en13092244.
Повний текст джерелаАнотація:
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 machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.
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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 (February 8, 2021): 390–401. http://dx.doi.org/10.1108/compel-07-2020-0246.
Повний текст джерелаАнотація:
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 obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated. Findings It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time. Originality/value The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.
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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.
Повний текст джерелаАнотація:
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 model. Through the analysis of the daily forecasting results, it is shown that the proposed method could reduce modeling error and forecasting error of SVM model effectively and has better performance than general methods.
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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.
Повний текст джерелаАнотація:
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 algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.
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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 (May 23, 2018): 3–21. http://dx.doi.org/10.1177/0142331218771141.
Повний текст джерелаАнотація:
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 characteristics of wind speed. A hybrid particle swarm optimization gravitational search algorithm (HPSOGSA) combining conventional PSOGSA with binary PSOGSA (BPSOGSA) is utilized to realize the FS and parameter optimization simultaneously. The PSOGSA is employed to tune the parameter combination of input weights and biases in ELM, while BPSOGSA is exploited to select the most suitable features from the candidate input variables determined by a partial autocorrelation function for reconstruction of the input matrix for ELM. The proposed forecasting strategy carries out multi-step short-term WSF using mean half-hour historical wind speed data collected from a wind farm situated in Anhui, China. To investigate the forecasting results of the hybrid model, a lot of comparisons and analyses are executed. Simulation results illustrate that the proposed WPD-ELM model with FS and parameter optimization can effectively catch the non-linear characteristics hidden in wind speed data and provide satisfactory WSF performance.
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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.
Повний текст джерелаАнотація:
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 employed to decompose the original wind time-series into sublayer modes. The binary bat algorithm (BBA) is used to complete the feature selection. Bayesian optimization (BO) fine-tuned online sequential extreme learning machine (OSELM) is proposed to forecast the low-frequency sublayers of VMD. Bagging-based ensemble OSELM is proposed to forecast high-frequency sublayers of VMD. Two experiments were conducted on 10 min datasets from the National Renewable Energy Laboratory (NREL). The performances of the proposed model were compared with various representative models. The experimental results indicate that the proposed model has better accuracy than the comparison models. Among the thirteen models, the proposed VMD-BBA-EnsOSELM model can obtain the best prediction accuracy, and the mean absolute percent error (MAPE) is always less than 0.09.
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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 (January 21, 2020): 529. http://dx.doi.org/10.3390/en13030529.
Повний текст джерелаАнотація:
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 tilt angle that maximizes the converted energy of photovoltaic (PV) systems. Considering various factors such as weather, dust level, and aerosol level, five forecasting models were constructed using linear regression (LR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and gradient boosting (GB). Using the best forecasting model, our model showed increase in PV output compared with optimal angle models.
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Jasiński, Tomasz, and Agnieszka Ścianowska. "Security assessment and optimization of energy supply (neural networks approach)." Oeconomia Copernicana 6, no. 2 (June 30, 2015): 129. http://dx.doi.org/10.12775/oec.2015.016.
Повний текст джерелаАнотація:
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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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 (January 1, 2022): 1–21. http://dx.doi.org/10.4018/ijsi.309735.
Повний текст джерелаАнотація:
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 features, higher order statistical features and proposed MI and entropy features” are extracted. Then, feature selection is done and appropriate features are elected via chi-square scheme. The elected features are detected via LSTM and DBN to predict the defects. The weights of LSTM and DBN are optimized by Opposite Behavior Learning Integrated SDO (OBLI-SDO) algorithm. Finally, examination is done to prove the betterment of OBLI-SDO.
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Wu, Kaitong, Xiangang Peng, Zilu Li, Wenbo Cui, Haoliang Yuan, Chun Sing Lai, and Loi Lei Lai. "A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection." Energies 15, no. 15 (July 27, 2022): 5410. http://dx.doi.org/10.3390/en15155410.
Повний текст джерелаАнотація:
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 feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.
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Deepika, Nalabala, and Mundukur Nirupamabhat. "An Optimized Machine Learning Model for Stock Trend Anticipation." Ingénierie des systèmes d information 25, no. 6 (December 31, 2020): 783–92. http://dx.doi.org/10.18280/isi.250608.
Повний текст джерелаАнотація:
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, dimensionality reduction and optimization techniques can be integrated with emerging advanced machine learning models, to get improvised prediction in terms of quality, performance, security and for effective assessment external factors role in stock market nonlinear signals. In this empirical research work, a set of hybrid models were built and their predictive abilities were compared to find consistent model. This work implies the base model, boosted model and deep learning model along with optimization techniques. From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by showing best Mean Absolute Percentage Error (MAPE) value, which was low.
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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 (July 2, 2021): 3992. http://dx.doi.org/10.3390/en14133992.
Повний текст джерелаАнотація:
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 voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.
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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 (February 28, 2023): 613. http://dx.doi.org/10.3390/sym15030613.
Повний текст джерелаАнотація:
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 utilization of GWA-T-12 and PlanetLab traces is used to assess the method’s efficacy. In terms of RMSE, the approach is compared to the LSTM, GRU, and BiLSTM sub-models.
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Motwakel, Abdelwahed, Eatedal Alabdulkreem, Abdulbaset Gaddah, Radwa Marzouk, Nermin M. Salem, Abu Sarwar Zamani, Amgad Atta Abdelmageed, and Mohamed I. Eldesouki. "Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids." Sustainability 15, no. 2 (January 12, 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. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
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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 (August 4, 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) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.
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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 construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.
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Calvo-Pardo, Hector F., Tullio Mancini, and Jose Olmo. "Neural Network Models for Empirical Finance." Journal of Risk and Financial Management 13, no. 11 (October 30, 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 also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.
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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 (September 29, 2022): 7170. http://dx.doi.org/10.3390/en15197170.
Повний текст джерелаАнотація:
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 self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error.
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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 (December 1, 2022): 3483–89. http://dx.doi.org/10.11591/eei.v11i6.3927.
Повний текст джерелаАнотація:
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 decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
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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 (September 25, 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 involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.
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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 (May 2, 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, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.
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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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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 (December 21, 2022): 57. http://dx.doi.org/10.3390/en16010057.
Повний текст джерелаАнотація:
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. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techniques using two different aggregated-level datasets and observing the effects of these methods on both. Case and comparison studies have been conducted on these high-performing methods before, but there are not many examples studied using data from two different structures. Although the data used in this study were different from each other in terms of the time step, they also had very different and varied features. In addition, feature selection was studied on both datasets and a backward-eliminated exhaustive approach based on the performance of the artificial neural network (ANN) on the validation set was proposed for the development study of the forecasting models. A new DL-based ensemble approach was proposed after examining the results obtained on two separate datasets by applying the feature selection approach to the working forecasting methods, and the numerical results illustrate that it can significantly improve the forecasting performance compared with these up-to-date methods.
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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 (September 1, 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 paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.
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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 (October 12, 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 modeling approach to reduce the mean absolute percentage error. We utilized hyperparameter optimization to generate a universal automated framework, distinct from model optimization techniques that rely on a single case study. The resulting model can be independently customized to any respective project. Automating much of this process minimizes the work and expertise required for traffic count forecasting. To test the applicability of our models, we used Florida historical traffic data from between 2001 and 2017. The results confirmed that nonlinear models outperformed linear models in predicting passenger vehicles’ monthly traffic volumes in this specific case study. By employing the framework developed in this study, transportation planners could identify the critical links on US roads that incur overcapacity issues.
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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 descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.
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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 (January 13, 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 collected electric data, which lead to accurate prediction results that outperform several alternative statistical and machine learning approaches. Unfortunately, applying LSTM models may not produce acceptable forecasting results, not only because of the noisy electric data but also due to the naive selection of its hyperparameter values. Therefore, an optimal configuration of an LSTM model is necessary to describe the electric consumption patterns and discover the time-series dynamics in the energy domain. Finding such an optimal configuration is, on the one hand, a combinatorial problem where selection is done from a very large space of choices; on the other hand, it is a learning problem where the hyperparameters should reflect the energy consumption domain knowledge, such as the influential time lags, seasonality, periodicity, and other temporal attributes. To handle this problem, we use in this paper metaheuristic-search-based algorithms, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain. We tailor both a genetic algorithm (GA) and particle swarm optimization (PSO) to learn hyperparameters for load forecasting in the context of energy consumption of big data. The statistical analysis of the obtained result shows that the multi-sequence deep learning model tuned by the metaheuristic search algorithms provides more accurate results than the benchmark machine learning models and the LSTM model whose inputs and hyperparameters were established through limited experience and a discounted number of experimentations.
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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 (September 1, 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: In step 1, the feature crossing and reinforcement learning methods are applied to select the suitable features that could efficiently shorten the redundancy of the input. In step 2, the stack denoising autoencoder is employed to extract deep fluctuation information in the features after the reinforcement learning. In step 3, the bidirectional gated recurrent unit algorithm is utilized to accomplish the forecasting model and achieve the final results. These parts of the integrated modeling structure contributed to increased forecasting accuracy than single models. By analyzing the forecasting results of three different data series, it could be summarized that: (1) The proposed two-stage feature selection method and feature extraction method could greatly optimize the input for the predictor and form the optimal axle temperature forecasting model. (2) The proposed hybrid model can achieve satisfactory forecasting results which are better than the contrast algorithms proposed by other researchers.
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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 crane load spectrum v-SVRM prediction model. Then, combining the improved fruit fly algorithm with penalty function and using anti-bound thought, a secondary optimization is carried out for three kind parameters. The results of examples engineering show that the optimal parameter group selected by the improved algorithm shortens the training time, reduces the computational complexity and improves the learning accuracy and generalization ability of v-SVRM model. The accuracy and stability of parameters is enhanced by secondary optimization, so as to a better robustness and versatility of v-SVRM predictive model. It also provides a new way for the establishment of efficient and convenient crane load spectrum v-SVRM forecast model.
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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 (February 21, 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 this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.
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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 (March 10, 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 bald eagle search (MBES) algorithm was established to construct an MBES-LSTM model, a short-term WP forecasting model to make predictions, so as to address the problem that the selection of LSTM hyperparameters may affect the forecasting results. After preprocessing the WP data acquired by SCADA, the MBES-LSTM model was used to forecast the WP. The experimental results reveal that, compared with the PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, and BES-LSTM forecasting models, the MBES-LSTM model could effectively improve the accuracy of WP forecasting for wind farms.
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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 (November 9, 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 selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.
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Almulihi, Ahmed, Hager Saleh, Ali Mohamed Hussien, Sherif Mostafa, Shaker El-Sappagh, Khaled Alnowaiser, Abdelmgeid A. Ali, and Moatamad Refaat Hassan. "Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction." Diagnostics 12, no. 12 (December 18, 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 heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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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 (June 10, 2019): 175–93. http://dx.doi.org/10.1108/ijicc-10-2018-0145.
Повний текст джерелаАнотація:
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 effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms. Findings Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study. Originality/value The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.
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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 (January 26, 2023): 36. http://dx.doi.org/10.3390/hydrology10020036.
Повний текст джерелаАнотація:
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., regression-based methods, artificial neural networks, etc.), where NFS have been reported to be comparable, if not superior, to other models. Despite successful applications and increasing popularity, the development of NFS models is still challenging due to a number of limitations. This study reviews different types of NFS algorithms and discusses the typical challenges in developing NFS-based hydrological models. The challenges in developing NFS models are categorized under six topics: data pre-processing, input selection, training data selection, adaptability, interpretability, and model parameter optimization. At last, future directions for enhancing NFS models are discussed. This review–prospective article gives a helpful overview of the suitability of NFS techniques for various applications in hydrological modeling and forecasting while identifying research gaps for future studies in this area.
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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 (September 14, 2022): 6721. http://dx.doi.org/10.3390/en15186721.
Повний текст джерелаАнотація:
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 dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.
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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 (December 22, 2021): 315–25. http://dx.doi.org/10.1007/s11740-021-01098-4.
Повний текст джерелаАнотація:
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 joints usually focusing on the optimization of particular tool geometries against shear and tensile loading. This mainly involved the application of a meta-model assisted genetic algorithm to define a solution space including Pareto optima with all efficient allocations. However, if the investigation of new process configurations (e. g. changing materials) is necessary, the earlier generated meta-models often reach their limits which can lead to a significantly loss of estimation quality. Thus, it is mainly required to repeat the time-consuming and resource-intensive data sampling process in combination with the following identification of best-fitting meta-modeling algorithms. As a solution to this problem, the combination of Deep and Reinforcement Learning provides high potentials for the determination of optimal solutions without taking labeled input data into consideration. Therefore, the training of an Agent aims not only to predict quality-relevant joint characteristics, but also at learning a policy of how to obtain them. As a result, the parameters of the deep neural networks are adapted to represent the effects of varying tool configurations on the target variables. This provides the definition of a novel approach to analyze and optimize clinch joint characteristics for certain use-case scenarios.
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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 (March 24, 2021): 1794. http://dx.doi.org/10.3390/en14071794.
Повний текст джерелаАнотація:
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 in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).
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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 (December 3, 2020): 6405. http://dx.doi.org/10.3390/en13236405.
Повний текст джерелаАнотація:
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 rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.
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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 (November 25, 2020): 701. http://dx.doi.org/10.3390/ijgi9120701.
Повний текст джерелаАнотація:
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 methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.
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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 (January 2019): 1–15. http://dx.doi.org/10.4018/ijirr.2019010101.
Повний текст джерелаАнотація:
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 implemented with the traditional tf-idf model and then with the novel feature optimization model. Benchmark Kaggle dataset, which includes a collection of tweets is considered to report the results. The results are assessed on the basis of performance accuracy. Empirical analysis validates that the proposed implementation of a binary cuckoo search for feature selection optimization in a sentiment analysis task outperforms the elementary supervised algorithms based on the conventional tf-idf score.
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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.
Повний текст джерелаАнотація:
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 historical period is processed; building the most accurate forecast of the actual amount of electricity for the day ahead; assessment of the forecast quality. Methods used: time series analysis, neural network modeling, short-term forecasting of energy consumption in the metallurgical industry. The results obtained: to develop a model for predicting the energy consumption of a metallurgical enterprise based on artificial neural networks, the MATLAB complex with the Neural Network Toolbox was chosen. When conducting experiments, based on the available statistical data of a metallurgical enterprise, a selection of architectures and algorithms for learning neural networks was carried out. The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. Another approach, deep learning, is also considered, namely the neural network with long short-term memory LSTM and the adam learning algorithm. Such a deep neural network allows you to process large amounts of input information in a short time and build dependencies with uninformative input information. The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. The use of models will allow automating high-precision operational hourly forecasting of energy consumption in market conditions.
Keywords: energy consumption; forecasting; artificial neural network; time series.