Short term load forecasting with markovian switching distributed deep belief networks

Dong, Yi; Dong, Zhen; Zhao, Tianqiao; Li, Zhongguo; Ding, Zhengtao

doi:10.1016/j.ijepes.2021.106942

Cited by 25 publications

(10 citation statements)

References 36 publications

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“…The communication protocols are crucial for coordination between electrical energy generation, distribution, and transmission. [137,138] In other words, these protocols comprise sets of hardware/software rules that communication allows the exchange of some data between the process members. [139] Thus, the wind power status can be reported, and the transmission operator can address the request power limit.…”

Section: Measurementmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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Section: Measurementmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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“…Yang J, Bao W, Liu Y, et al proposed to use Markov network to establish a probability transfer model between LR Patch and HR Patch, as well as HR Patch and its neighbors, and use a large amount of training data to learn and train it [14]. Dong Y, Dong Z, Zhao T, et al proposed the idea of "phantom face," which can obtain high-resolution images up to 8 times, but it is targeted and not universal [15]. Wang W, Tian W, Liao W, et al proposed a writing adaptive technique with discriminating ability.…”

Section: Related Workmentioning

confidence: 99%

Application of Improved Deep Belief Network Model in 3D Art Design

2022

Mathematical Problems in Engineering

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In recent years, driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used in 3D art design and other fields, and a large number of breakthrough results have emerged. 3D art design is a research hotspot in the field of computer vision, which has broad application prospects and practical application value. Aiming at the problems of slow convergence and long training time of traditional deep belief network in the process of data feature expression, this paper proposes an unsupervised learning algorithm, namely adaptive deep belief network, and applies it to 3D art design. Its linear correction unit has good sparsity, which can improve the network performance well. The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM. The recognition research of 3D art design by optimizing the wavelet deep belief network can effectively improve the recognition rate and recognition speed of handwritten character recognition and achieve good results.

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“…The SLF problem has been also studied using a variety of sophisticated data-driven models. Fuzzy logic [24], artificial neural networks (ANNs) [3,7], Extreme Learning [17,22], and exponential smoothing [25] are some of the models that have been used. Numerous hybrid models have been developed by combining multiple models in order to increase forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap aggregating approach to short-term load forecasting using meteorological parameters for demand side management in the North-Eastern Region of India

Sarkar

Gunturi

2022

Theor Appl Climatol

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Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland's load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that

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Short term load forecasting with markovian switching distributed deep belief networks

Cited by 25 publications

References 36 publications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

Application of Improved Deep Belief Network Model in 3D Art Design

Bootstrap aggregating approach to short-term load forecasting using meteorological parameters for demand side management in the North-Eastern Region of India

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