MATEC Web Conf.
Volume 335, 202114th EURECA 2020 – International Engineering and Computing Research Conference “Shaping the Future through Multidisciplinary Research”
|Number of page(s)||12|
|Section||Electric & Electronic Engineering|
|Published online||25 January 2021|
Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks
1 Taylor’s University, 1, Jalan Taylors, 47500 Subang Jaya, Selangor, Malaysia
2 GoAutomate Sdn Bhd, 33, Jalan Pentadbir U1/30, Taman Perindustrian Batu 3, 40150 Shah Alam, Selangor, Malaysia
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2021
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