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.

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…In order to attain these goals, scientists have used different methods and carried out numerous performance improvement techniques, such as: SVR [3,11,28], LSTM ANNs [3,4,10,11,26,28], RNN [5,28], BiLSTM ANN [5], Copula-DBN [7], DRNN-LSTM [8], CNN-RNN [9], the Prophet and Holt-Winters long-term forecasting models [13], CNNs [4,6,14,25], MLR [24,25], SVM [24], and ANFIS [24]. In contrast with these, within our article, we devised and developed a forecasting method for the hourly month-ahead electricity consumption based on a BiLSTM ANN enhanced with a multiple simultaneously decreasing delays approach coupled with FITNETs.…”

“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

“…In a recent paper, Ribeiro et al started their research by highlighting the importance of benefitting from accurate energy consumption forecasts in the case of companies that are energy intensive consumers [28]. In addition, the authors highlight the importance of implementing DL techniques in energy demand forecasting.…”

Intelligent Sensors for Sustainable Food and Drink Manufacturing

“…There are challenges around improving the energy efficiency of common processes such as refrigeration (Tsamos et al, 2017), drying (Sun et al, 2019), and frying (Su et al, 2018) whilst other processes such as washing and cleaning are hugely water-intensive (Simeone et al, 2018). Within food and drink manufacturing, examples of IDT use to improve resource efficiency include forecasting energy consumption to inform mitigation measures (Ribeiro et al, 2020), reducing energy consumption in drying (Sun et al, 2019), reducing the amount of product lost by automating product quality testing (García-Esteban et al, 2018) and reducing water consumption in agriculture via Industrial Internet of Things (IIoT) monitoring (Jha et al, 2019). Although time is rarely discussed in sustainable manufacturing discussions, it is a key aspect as reducing the time of a process reduces its resource demand and the associated overheads (e.g., lighting and heating a factory).…”

Indonesian load prediction estimation using long short term memory

“…Long-short term memory (LSTM) by combining ISSN: 2252-8938  Indonesian load prediction estimation using long short term memory (Erliza Yuniarti) 1027 short-term memory with long-term memory, through gate control prevents signal loss during the prediction process, so as to a better accuracy [14]- [16]. Gate recurrent unit (GRU) is applied in the demand side energy forecasting which is still limited [17], predict electrical power load [18], shows the prediction performance of GRU is still lower than LSTM, but better than traditional models. Modeling in particular with time-series data sets using the LSTM technique is quite popular for solving complex sequence models such as electrical loads, by studying long-term dependencies and tracing patterns that occurred far in the past.…”