Commercial Vacancy Prediction Using LSTM Neural Networks

Lee, Jaekyung; Kim, H; Kim, Hyungkyoo

doi:10.3390/su13105400

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…RNNs suffer from vanishing and exploding gradient problems; therefore, to handle these issues, the architecture of LSTM has been introduced, which is well known for its good performance on sequential problems with long-term dependencies [60]. The hidden layer of LSTM, which is also called the LSTM cell, makes it different from the general RNN architecture [61]. Figure 3a shows the hidden layer of LSTM, where x t is the input of the cell at time t, and h t is the output.…”

Section: Long Short-term Memorymentioning

confidence: 99%

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

et al. 2021

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Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

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Section: Long Short-term Memorymentioning

confidence: 99%

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

et al. 2021

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“…Therefore, to guarantee the feasibility of photovoltaic power forecasting, it is beneficial to fully analyze the impact of environmental factors on the modeling of photovoltaic power forecasting. Moreover, the long short-term memory (LSTM) network, as referenced in the literature [28,29], represents a type of deep neural network. Within the framework of deep learning models, the LSTM network stands out for its exceptional proficiency in addressing issues related to time series forecasting, attributable to its distinctive architectural design.…”

Section: Introductionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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Inferring storefront vacancy using mobile sensing images and computer vision approaches

Li,

Long

2024

Computers, Environment and Urban Systems

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Commercial Vacancy Prediction Using LSTM Neural Networks

Cited by 5 publications

References 35 publications

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Inferring storefront vacancy using mobile sensing images and computer vision approaches

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