Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation

2021 ◽  
pp. 103227
Author(s):  
Muxing Zhang ◽  
Xiaosong Zhang ◽  
Siyi Guo ◽  
Xiaodong Xu ◽  
Jiayu Chen ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Building Energy ◽  
Short Term ◽  
Energy Estimation ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Long Term Monitoring ◽  
Term Monitoring

Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network

2019 ◽  
Vol 239 ◽  
pp. 181-191 ◽  
Author(s):  
Shuang Han ◽  
Yan-hui Qiao ◽  
Jie Yan ◽  
Yong-qian Liu ◽  
Li Li ◽  
...  
Keyword(s):  
Power Generation ◽  
Short Term Memory ◽  
Copula Function ◽  
Short Term ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4373
Author(s):  
Muhammad Aslam ◽  
Jae-Myeong Lee ◽  
Mustafa Altaha ◽  
Seung-Jae Lee ◽  
Sugwon Hong
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Degradation Rate ◽  
Short Term Memory ◽  
Short Term ◽  
Pv System ◽  
Energy Estimation ◽  
Term Memory ◽  
Long Short Term Memory

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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