Paper Info
Title
Short-Term Firm-Level Energy-Consumption Forecasting For Energy-Intensive Manufacturing: A Comparison Of Machine Learning And Deep Learning Models
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.
Year
DOI
Venue
2020
10.3390/a13110274
ALGORITHMS
Keywords
DocType
Volume
short-term load forecasting, STLF, deep learning, RNN, LSTM, GRU, machine learning, SVR, random forest, energy consumption, energy-intensive manufacturing, time-series prediction, industry
Journal
13
Issue
Citations 
PageRank 
11
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Andrea Maria Nogueira Cavalcanti Ribeiro100.34
Pedro Rafael X. do Carmo200.34
Iago Richard Rodrigues Silva300.34
Djamel Sadok437157.81
Theo Lynn511622.40
Patricia Endo612125.32