Abstract
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.
Highlights
The industrial sector is the largest consumer of delivered energy worldwide, and energy-intensive manufacturing is the largest component in that sector [1]
We propose three deep-learning models—simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU)—and two machine-learning models—Support Vector
Root mean squared error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE) are the most commonly metrics used in the evaluation of the accuracy of energy-consumption models [14] and, in particular, studies related to short-term load forecasting (STLF) using deep learning [6,7,15]
Summary
C. Ribeiro 1 , Pedro Rafael X. do Carmo 1 , Iago Richard Rodrigues 1 , Djamel Sadok 1 , Theo Lynn 2 and Patricia Takako Endo 3, *. Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Pernambuco 50100-010, Brazil. Received: 20 September 2020; Accepted: 28 October 2020; Published: 30 October 2020
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