scholarly journals Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation

2021 ◽  
Vol 11 (13) ◽  
pp. 5930
Author(s):  
Tzu-Lun Yuan ◽  
Dian-Sheng Jiang ◽  
Shih-Yun Huang ◽  
Yuan-Yu Hsu ◽  
Hung-Chih Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Moving Average ◽  
Apparent Temperature ◽  
Time Interval ◽  
Forecast Errors ◽  
Short Term ◽  
Load Forecast ◽  
Temperature Bias

Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications.

scholarly journals Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 668 ◽  
Author(s):  
S. Poornima ◽  
M. Pushpalatha
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Long Short Term Memory

Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

scholarly journals Real Time Prediction of Sclera Force with LSTM Neural Networks in Robot-Assisted Retinal Surgery

2020 ◽  
Vol 896 ◽  
pp. 183-194
Author(s):  
Chang Yan He ◽  
Niravkumar Patel ◽  
Marin Kobilarov ◽  
Iulian Iordachita
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Contact Force ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
User Behavior ◽  
Short Term ◽  
Insertion Depth ◽  
Term Memory ◽  
Long Short Term Memory

Retinal microsurgery is one of the most technically demanding surgeries, during which the surgical tool needs to be inserted into the eyeball and is constantly constrained by the sclerotomy port. During the surgery, any unexpected manipulation could cause extreme tool-sclera contact force leading to sclera damage. Although, a robot assistant could reduce hand tremor and improve the tool positioning accuracy, it cannot prevent or alarm the surgeon about the upcoming danger caused by surgeon’s misoperations, i.e., applying excessive force on the sclera. In this paper, we present a new method based on a Long Short Term Memory recurrent neural network for predicting the user behavior, i.e., the contact force between the tool and sclera (sclera force) and the insertion depth of the tool from sclera contact point (insertion depth) in real time (40Hz). The predicted force information is provided to the user through auditory feedback to alarm any unexpected sclera force. The user behavior data is collected in a mock retinal surgical operation on a dry eye phantom with Steady Hand Eye Robot and a novel multi-function sensing tool. The Long Short Term Memory recurrent neural network is trained on the collected time series of sclera force and insertion depth. The network can predict the sclera force and insertion depth 100 milliseconds in the future with 95.29% and 96.57% accuracy, respectively, and can help reduce the fraction of unsafe sclera forces from 40.19% to 15.43%.

scholarly journals Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

2021 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Rusul Abduljabbar ◽  
Hussein Dia ◽  
Pei-Wei Tsai ◽  
Sohani Liyanage
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Data Science ◽  
Real Life ◽  
Decision Makers ◽  
Sensor Data ◽  
Superior Performance ◽  
Short Term ◽  
Traffic Forecasting

Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.

Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems

2016 ◽  
Vol 31 (1) ◽  
pp. 72-81 ◽  
Author(s):  
Ni Ding ◽  
Clementine Benoit ◽  
Guillaume Foggia ◽  
Yvon Besanger ◽  
Frederic Wurtz
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Model Design ◽  
Short Term ◽  
Load Forecast
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