A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting

Lu, Jixiang; Zhang, Qipei; Zhihong, Yang; Tu, Mengfu

doi:10.1109/pesgm40551.2019.8973549

Cited by 32 publications

(14 citation statements)

References 12 publications

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“…The decreasing spatial dimension in the max-pooling layer could decrease the computation cost. The CNN layers could convert the higher-dimension original historical data into shorter feature maps, which provide useful relevant information with target data [54]. The output information is transported to the LSTM layers, which could effectively process the sequential information.…”

Section: The Developed Convolution Neural Network and Lstm Algorithmsmentioning

confidence: 99%

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Nguyen-Da

Peng

et al. 2023

Sustainability

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The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.

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Section: The Developed Convolution Neural Network and Lstm Algorithmsmentioning

confidence: 99%

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Nguyen-Da

Peng

et al. 2023

Sustainability

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“…LSTM designed to use long-range data regions involving data from time-series, which presents the best encouragement and potential for the evolution regarding a suitable arrangement to nonlinear structural challenges. The LSTM model supplants every RNN system under the hidden layer that contains an LSTM block for making a long-term memory [24]. The LSTM block contains four interactive parts: which is an input gate, an output gate, a forgetting gate, and an internal unit.…”

Section: Neural Networkmentioning

confidence: 99%

Predicting temperature of Erbil City applying deep learning and neural network

Jumur

Kareem

Yousif

2021

IJEECS

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<span>One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on <span id="docs-internal-guid-850bd062-7fff-c6c8-9146-ba6427eb24e0" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">long short-term memory</span> (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and <span id="docs-internal-guid-14d37b98-7fff-0f76-848f-ad9f89224f77" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> generalized regression neural network</span> (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.</span>

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“…In addition, some scholars have proposed hybrid model-based electricity consumption forecasting methods [19][20][21]. Reference [22] adopted a strategy based on a hybrid CNN and LSTM applied to shortterm load forecasting. A hybrid model-based prediction method has less prediction inaccuracy than LSTM.…”

Section: Introductionmentioning

confidence: 99%

Research on Short-term Time Series Forecasting Method Based on Feature Fusion of Electric Power

Ru¹,

Qin

Lü

et al. 2023

J. Phys.: Conf. Ser.

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In the power industry, time series forecasting has many application scenarios, such as accurate electricity consumption forecasting and load forecasting, which can provide a reference for power grid staff and reduce decision-making costs. To improve the accuracy of short-term load forecasting, this paper proposes a hybrid forecasting model based on an improved encoder-decoder architecture model and a Temporal Convolutional Network. Based on the gate recurrent unit, the model in this paper introduces the encoder-decoder architecture, adds the attention mechanism for improving the prediction accuracy, and performs feature fusion with the Temporal Convolutional Network, building a dual-feature fusion model finally. Experiments show that the method proposed in this paper reduces the evaluation metrics RMSE, MAPE and MAE by 7.7%, 15.2%, and 13.9% respectively compared to the Long Short-Term Memory Networks. Compared with the Temporal Convolutional Network, it is reduced by 2.6%, 4.8%, and 4%.

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A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting

Cited by 32 publications

References 12 publications

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

Predicting temperature of Erbil City applying deep learning and neural network

Research on Short-term Time Series Forecasting Method Based on Feature Fusion of Electric Power

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