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Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

1
Centro de Informática, Universidade Federal de Pernambuco, Pernambuco 50670-901, Brazil
2
Irish Institute of Digital Business, Dublin City University, 9 Dublin, Ireland
3
Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Pernambuco 50100-010, Brazil
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(11), 274; https://doi.org/10.3390/a13110274
Received: 20 September 2020 / Revised: 23 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
To minimise environmental impact, to avoid regulatory penalties, and to improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time-series models due to its high dimensionality and problem-solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA) and an existing manual technique used at the case site) against three deep-learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and two machine-learning models (Support Vector Regression (SVR) and Random Forest) for short-term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model and then use Diebold–Mariano testing to confirm the results. The results suggests that the legacy approach used at the case site is the worst performing and that the GRU model outperformed all other models tested. View Full-Text
Keywords: short-term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; energy consumption; energy-intensive manufacturing; time-series prediction; industry short-term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; energy consumption; energy-intensive manufacturing; time-series prediction; industry
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MDPI and ACS Style

Ribeiro, A.M.N.C.; do Carmo, P.R.X.; Rodrigues, I.R.; Sadok, D.; Lynn, T.; Endo, P.T. Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. Algorithms 2020, 13, 274. https://doi.org/10.3390/a13110274

AMA Style

Ribeiro AMNC, do Carmo PRX, Rodrigues IR, Sadok D, Lynn T, Endo PT. Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. Algorithms. 2020; 13(11):274. https://doi.org/10.3390/a13110274

Chicago/Turabian Style

Ribeiro, Andrea M.N.C.; do Carmo, Pedro R.X.; Rodrigues, Iago R.; Sadok, Djamel; Lynn, Theo; Endo, Patricia T. 2020. "Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models" Algorithms 13, no. 11: 274. https://doi.org/10.3390/a13110274

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