Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series

Sadaei, Hossein Javedani; Silva, Petrônio Cândido de Lima e; Guimarães, Frederico Gadelha; Lee, Muhammad Hisyam

doi:10.1016/j.energy.2019.03.081

Cited by 226 publications

(58 citation statements)

References 32 publications

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“…During the hunting, it first calculates the distance between individuals within the group and α, β, and δ from equations (10)- (15) and comprehensively determines the direction in which the individual moves toward the prey by using equation (16).…”

Section: Definition 2 Encircling Preymentioning

confidence: 99%

“…Step 4: update a, A → , and C → by equations 7and (9). Update all the search agents by equation (16). Update t � t + 1.…”

Section: Gwo-based Multiple Kernel Extreme Learning Machinementioning

confidence: 99%

“…erefore, they have become more and more popular in energy forecasting. Typical AI models include support-vector regression (SVR) and its extension least-squares SVR (LSSVR) [5,6], artificial neural network (ANN) [7][8][9], extreme learning machine (ELM) [10], sparse Bayesian learning (SBL) [11,12], deep learning [13] (stacked denoising autoencoders (SDAs) [14], deep belief network (DBN) [15], convolutional neural network (CNN) [16], and long short-term memory (LSTM)) [17]), and nature-inspired optimization algorithms [18,19]. For example, Chen et al put forward a new SVR model that used the temperature before demand response as additional input variables for STLF [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

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Section: Definition 2 Encircling Preymentioning

confidence: 99%

“…Step 4: update a, A → , and C → by equations 7and (9). Update all the search agents by equation (16). Update t � t + 1.…”

Section: Gwo-based Multiple Kernel Extreme Learning Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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“…Smart and effective energy management systems require reliable load forecasting. For this reason, Sadaei et al [24] suggested combining fuzzy time series and CNN for short-term load forecasting and they obtained successful results. Bayr and Puschmann [25] applied CNN classification to landscape photographs for detecting woody regrowth vegetation and emphasized the contribution of a robust CNN algorithm in landscape monitoring together with satellite imagery and field measurements.…”

Section: Previous Studies On the Use Of Cnn In Geosciencesmentioning

confidence: 99%

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

Can

Kocaman

Gökçeoğlu

2019

IJGI

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Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

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“…These modern methods are getting more popular among researchers when dealing with time series forecasting [23]. These artificial intelligence models can achieve good forecasting performance because of their unique characteristics, such as memory, self-learning, and self-adaptability, since the neural networks are products of biological simulation that follow the behavior of the human brain [24]. Park [25] showed good performance of this type of model after first applying ANNs in power load forecasting in 1991.…”

Section: Literature Reviewmentioning

confidence: 99%

Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

Zhang

Wang

2019

Energies

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Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

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Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series

Cited by 226 publications

References 32 publications

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

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