Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 20 September 2020 / Approved: 21 September 2020 / Online: 21 September 2020 (04:19:45 CEST)

How to cite: Ribeiro, A.M.; Carmo, P.R.; Rodrigues, I.; 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. Preprints 2020, 2020090491 (doi: 10.20944/preprints202009.0491.v1). Ribeiro, A.M.; Carmo, P.R.; Rodrigues, I.; 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. Preprints 2020, 2020090491 (doi: 10.20944/preprints202009.0491.v1).

Abstract

To minimise environmental impact, avoid regulatory penalties, and 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 three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) 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. 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.

Subject Areas

Short term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; KNN; energy consumption; energy-intensive manufacturing; time series prediction; industry

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