Log in

Relevant bibliographies by topics / Optimization, Forecasting, Meta Learning, Model Selection

Contents

Journal articles

Academic literature on the topic 'Optimization, Forecasting, Meta Learning, Model Selection'

Author: Grafiati

Published: 10 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Optimization, Forecasting, Meta Learning, Model Selection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Optimization, Forecasting, Meta Learning, Model Selection"

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!