Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Fan, Shilong

doi:10.1109/access.2019.2949030

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…The power industry is a pillar industry for social development. With the upgrading of energy consumption in various industries [1], there is a certain imbalance between the rapid growth of energy demand and the construction of regional power grids. Specially, the regional power grids with concentrated loads or power sources operate under extreme conditions.…”

Section: A Summary Of Transmission Expansion Network Planningmentioning

confidence: 99%

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Wang

Zhou

et al. 2021

IEEE Access

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Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixedinteger linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of the heuristic learning-based methods are inexplicable and usually require repeated training. By using DDQN-ResNet, this paper constructs a high-performance and flexible method to solve large-scale and complex-constrained TNEP problem. Firstly, we form a two-objective TNEP model, in which one objective is to minimize the comprehensive cost, and another objective is to maximize the transmission network reliability. The comprehensive cost takes into account the expansion cost, the network loss cost, and the maintenance cost. The transmission network reliability is evaluated by the expected energy not served (EENS) and the electrical betweenness. Secondly, from the TNEP model, the TNEP task is constructed based on the Markov decision process. By abstracting the task, the TNEP environment is obtained for DDQN-ResNet. In addition, to identify the construction value of lines, an agent is establish based on DDQN-ResNet. Finally, we perform the static planning and visualize the reinforcement learning process. The dynamic planning is realized by reusing the training experience. The validity and flexibility of DDQN-ResNet are verified on RTS 24-bus test system.

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Section: A Summary Of Transmission Expansion Network Planningmentioning

confidence: 99%

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Wang

Zhou

et al. 2021

IEEE Access

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“…Machine learning methods (such as BP, SVM, and RF) are shallow networks, and the prediction accuracy still needs to be improved. Deep-learning-based prediction methods are mostly used for the prediction of time series data, such as the prediction of building energy consumption using LSTM [22][23][24], predicting residential electricity consumption using CNN and LSTM [25][26][27], and predicting building energy consumption using GAN networks [28]. Alternatively, a comparison between traditional machine learning and deep learning methods finds that deep learning prediction methods have better results.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Zhang

Yan

et al. 2023

Sustainability

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Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME).

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“…e autonomous control platform is based on the deep learning network to complete the effective prediction of building energy consumption [14][15][16]. At present, some scholars have carried out research and analysis on building energy consumption based on the data-driven method of the deep network.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

Fan

Xing

2021

Scientific Programming

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Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

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Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

Cited by 15 publications

References 26 publications

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

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