Short-term Tie-line Power Prediction Based on CNN-LSTM

Huang, He; Lv, Yaming

doi:10.1109/ei250167.2020.9346998

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Standard recurrent neural network (RNN) becomes weak to learn dependencies hence the problem of exploding gradient and vanishing gradient arises when there is an increase in the time duration. State units, input gates, output gates and forget gates are introduced in LSTM to overcome the problem of vanishing gradient [34]. The Diagram of the input gate, output gate, and forget gate used in LSTM is shown in Fig.…”

Section: The Proposed Networkmentioning

confidence: 99%

Automatic Crop Expert System Using Improved LSTM with Attention Block

Sikandar¹,

Mahum²,

Aladhadh³

2023

Computer Systems Science and Engineering

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Agriculture plays an important role in the economy of any country. Approximately half of the population of developing countries is directly or indirectly connected to the agriculture field. Many farmers do not choose the right crop for cultivation depending on their soil type, crop type, and climatic requirements like rainfall. This wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the country. Many parameters should be observed such as soil characteristics, type of crop, and environmental factors for the cultivation of the right crop. Manual decision-making is time-taking and requires extensive experience. Therefore, there should be an automated system for the right crop recommendation to reduce human efforts and loss. An automated crop recommender system should take these parameters as input and suggest the farmer's right crop. Therefore, in this paper, a long short-term memory Network with an attention block has been proposed. The proposed model contains 27 layers, the first of which is a feature input layer. There exist 25 hidden layers between them, and an output layer completes the structure. Through these levels, the proposed model enables a successful recommendation of the crop. Additionally, the dropout layer's regularization properties aids in reduction of overfitting of the model. In this paper, a customized novel long short-term memory (LSTM) model is proposed with a residual attention block that recommends the right crop to farmers. Evaluation metrics used for the proposed model include f1-score, recall, precision, and accuracy attaining values as 95.69%, 96.56%, 96.9%, and 97.26% respectively.

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Section: The Proposed Networkmentioning

confidence: 99%

Automatic Crop Expert System Using Improved LSTM with Attention Block

Sikandar¹,

Mahum²,

Aladhadh³

2023

Computer Systems Science and Engineering

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Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Awais,

Mahum,

Zhang

et al. 2024

Sci Rep

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Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

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Design of a Convolutional Neural Network and a Modified Genetic Algorithm for Power Grid Disturbance Classification

Abegaz,

Muller

2024

2024 IEEE International Conference on Electro Information Technology (eIT)

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Short-term Tie-line Power Prediction Based on CNN-LSTM

Cited by 4 publications

References 7 publications

Automatic Crop Expert System Using Improved LSTM with Attention Block

Automatic Crop Expert System Using Improved LSTM with Attention Block

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Design of a Convolutional Neural Network and a Modified Genetic Algorithm for Power Grid Disturbance Classification

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