Short-Term Load Interval Prediction Using a Deep Belief Network

Zhang, Xiaoyu; Shu, Zhe; Wang, Rui; Zhang, Tao; Zha, Yabing

doi:10.3390/en11102744

Cited by 3 publications

(2 citation statements)

References 44 publications

(46 reference statements)

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“…As the nonlinearity and uncertainty of the power dataset increase, the difficulty of obtaining accurate load forecasting results increases. The accuracy of the prediction results has always been a process that needs to be continuously improved, from the traditional regression prediction method to the current deep learning algorithm [23,24]. The prediction method is improving constantly.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

Research on Short-Term Load Prediction Based on Seq2seq Model

Gong

Mahato

et al. 2019

Energies

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Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

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Section: Motivation and Problem Statementmentioning

confidence: 99%

Research on Short-Term Load Prediction Based on Seq2seq Model

Gong

Mahato

et al. 2019

Energies

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“…When using meta‐heuristic algorithms to train the LUBE models, 24,25 single objective, 7 or bi‐objective 26 functions are set by simply selecting the evaluation indexes. Heuristic algorithms including particle swarm optimization (PSO), 7,27 simulated annealing (SA), 21 a bat algorithm (BA), 19 an artificial bee algorithm, 26 and a genetic algorithm, 28 have been successfully adopted to solve the objective function in the training of LUBE models. Owing to the characteristics of swarm‐based optimization, meta‐heuristic algorithms are easy to implement and can solve many types of objective functions, even those that cannot be handled by the GD method, but may suffer from a low efficiency 29,30 .…”

Section: Introductionmentioning

confidence: 99%

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

Liu

et al. 2020

Wind Energy

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As a clean and renewable energy source, wind energy has achieved remarkable growth around the world. Wind power/speed interval prediction has become an indispensable area of focus regarding the efficient dispatch of wind energy. As an important interval prediction method, the traditional lower and upper bound estimation (LUBE) has been a prevalent approach and a fundamental branch of energy prediction. However, the traditional LUBE model suffers from a low training efficiency owing to a lack of the gradient descent (GD) training mechanism. In this study, an improved LUBE model was designed using a novel training scheme based on the GD method for better efficiency and greater prediction performance. Initially, the new objective functions, which are continuous and differential, meeting the requirements of the GD method, were designed to obtain the best prediction interval (PI) quality with a narrower PI width and greater coverage probability. Then, different loss function forms have been proposed and compared, with the new Huber loss function having been confirmed to be more effective than other traditional loss functions. Finally, the new LUBE model with an objective part and adapting to the GD training method was constructed. Both traditional and improved LUBE models with different loss functions were compared experimentally, and the results indicate that the improved LUBE model with a Huber loss function significantly reduces the training time and improves the quality of the PI.

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Short-time Series Load Forecasting By Seq2seq-LSTM Model

Cui

Yin

et al. 2020

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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Short-Term Load Interval Prediction Using a Deep Belief Network

Cited by 3 publications

References 44 publications

Research on Short-Term Load Prediction Based on Seq2seq Model

Research on Short-Term Load Prediction Based on Seq2seq Model

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

Short-time Series Load Forecasting By Seq2seq-LSTM Model

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