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Aug 9, 2023 ， This work proposes load forecasting models that rely on deep neural networks (DNNs). These models are applied to a demand-side load database for analysis.
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What is a bidirectional long short term memory network?
Bi-LSTM (Bidirectional Long Short-Term Memory) is a type of recurrent neural network (RNN) that processes sequential data in both forward and backward directions. It combines the power of LSTM with bidirectional processing, allowing the model to capture both past and future context of the input sequence.
What is the difference between LSTM and bidirectional LSTM?
BiLSTM adds one more LSTM layer, which reverses the direction of information flow. It means that the input sequence flows backward in the additional LSTM layer, followed by aggregating the outputs from both LSTM layers in several ways, such as average, sum, multiplication, or concatenation.
Is LSTM good for long term forecasting?
Demand data sets are featured with different seasonalities. The LSTM is capable of capturing the patterns of both long term seasonalities such as a yearly pattern and short term seasonalities such as weekly patterns.
What is long short term memory for forecasting?
LSTM architectures are capable of learning long-term dependencies in sequential data, which makes them well-suited for tasks such as language translation, speech recognition, and time series forecasting.
Nov 14, 2023 ， This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting.
Jun 1, 2023 ， This paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM.
Developing a hybrid load forecasting model based on deep learning applications called variational autoencoder bidirectional long short-term memory.
Aug 4, 2021 ， To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model.
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Dec 13, 2021 ， In this section, BiLSTM is developed to predict future speed, traffic count and occupancy for up to 60 min into the future. As mentioned before, ...
Jun 25, 2023 ， Yes, bidirectional LSTM (Long Short-Term Memory) can be used for time series analysis and prediction. LSTM is a type of recurrent neural network ...
The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models.
Feb 24, 2024 ， This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends.
The model can predict according to the trend of energy use data properly due to the data ratio of 100:100. Precise prediction of energy demand can be utilized ...