scholarly journals Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks

2021 ◽  
Vol 335 ◽  
pp. 02003
Author(s):  
Kai Lok Lum ◽  
Hou Kit Mun ◽  
Swee King Phang ◽  
Wei Qiang Tan
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Time Series Data ◽  
Electrical Energy ◽  
Series Data ◽  
Percentage Error ◽  
Electrical Energy Consumption ◽  
The Neural Network ◽  
Industrial Energy ◽  
Industrial Energy Consumption

In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory.

scholarly journals Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Deciphering anomalous heterogeneous intracellular transport with neural networks

2019 ◽  
Author(s):  
Daniel S Han ◽  
Nickolay Korabel ◽  
Runze Chen ◽  
Mark Johnston ◽  
Viki J. Allan ◽  
...  
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Intracellular Transport ◽  
Time Series Data ◽  
Series Data ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Data Points

AbstractBiological intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behaviour. Characterisation of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity adequately. Here, we have developed a deep learning feedforward neural network trained on fractional Brownian motion, which provides a novel, accurate and efficient characterization method for resolving heterogeneous behaviour of intracellular transport both in space and time. Importantly, the neural network requires significantly fewer data points compared to established methods, such as mean square displacements, rescaled range analysis and sequential range analysis. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, we were able to interpret anomalous intracellular dynamics as fractional Brownian motion with a stochastic Hurst exponent.

scholarly journals Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based

2018 ◽  
Vol 73 ◽  
pp. 13008 ◽  
Author(s):  
Hasbi Yasin ◽  
Budi Warsito ◽  
Rukun Santoso ◽  
Suparti
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Autoregressive Model ◽  
Stock Price ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Price Data

Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each number of neurons in hidden layer, the looping process is performed several times in order to get the best result. The best one is chosen by the least of Mean Absolute Percentage Error (MAPE) criteria. In this study, the model is applied in the two series of stock price data from Indonesia Stock Exchange. Evaluation of VAR-NN performance was based on train-validation and test-validation sample approach. Based on the empirical stock price data it can be concluded that VAR-NN yields perfect performance both in in-sample and in out-sample for non-linear function approximation. This is indicated by the MAPE value that is less than 1% .

scholarly journals Toward an embedded system for gesture recognition based on artificial neural network using reconfigurable target (case study and review)

2020 ◽  
Vol 10 (4) ◽  
pp. 142-148
Author(s):  
Ahmad Reda ◽  
Tareq Alshoufi ◽  
Ahmed Bouzid ◽  
József Vásárhelyi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Embedded System ◽  
Time Series Data ◽  
Reference Signal ◽  
Series Data ◽  
The Neural Network ◽  
Human Arm ◽  
On Chip

With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).

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