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, 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.

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

“…These metrics are commonly used in previous studies related to load forecasting (see, for example, [20], [27], [28], and [9]) and specifically in relation to short term load forecasting at commercial building levels [7,29].…”

“…More recently, the development of deep learning methods have provided further performance improvements thanks to their capacity to extract a variety of features from large datasets [7]. These models include (i) Convolutional Neural Networks (CNN) which perform particularly well in terms of feature extraction and generalisation [45], and (ii) Recurrent Neural Networks (RNN) which use information and patterns embedded in the time series itself to perform tasks that other ANNs are unable to do [9]. While deep learning models tend to outperform more traditional machine learning algorithms, they are not without limitations.…”

“…Such models process an input sequence at a time and maintain hidden units in their state vector that contain information about the history of all the past elements of the sequence [65]. This means that the the decision, classification, or learning done at a given time will influence the decision, classification, or learning at a subsequent time [9]. However, RNNs suffer from gradient vanishing issues which means that the weights propagated forward and backward through the layer tend to decrease, and therefore the algorithm cannot preserve long-range dependencies [46].…”

“…GRUs are less complex than LSTMs as they only use two gates, namely update and reset gate [52] (see Figure 6). GRU models transfer time dependencies in the data between different time steps by a single hidden state and are trained to selectively filter out any irrelevant information while maintaining what is useful [9]. Following the same approach presented in Section 3.1, we implemented a grid search method to determine the most suitable configuration for each deep learning model [70][71][72][73][74].…”

“…Energy load forecasting applications can be categorised into four main categories based on the time length of the prediction (forecasting horizon): (i) very short-term load forecasting (VSTLF) (minutes to hours), (ii) short-term load forecasting (STLF) (hours to days), (iii) medium term load forecasting (weeks to months), and (iv) long-term load forecasting (months to years) [7,8]. Similarly, load forecasting applications can be classified as either supply-or demand-side based on whether the focus is on energy production or consumption [9]. This paper specifically focuses on short and very short term demand-side load forecasting.…”