## 5 Related Work
While data for boiler energy, cooling energy and machine scheduling were taken in to account,again, unlike our work, specific process-related energy consumption was not considered and the focus was not energy-intensive manufacturing. Additionally, [PERSON] & [PERSON] [60] use simulated data whereas we use ground truth data to train and validate the models. In contrast to [6; 60], [PERSON] et al. [7] study the use of DNN for STLF in an energy-intensive manufacturing use case. Using data from the melt shop of steel plant, they sought to use DNN to predict energy consumption for one specific process, the electric arc furnace (EAF) for different types of scrap. The performance of the DNN was compared with linear regression (LR), SVM, and decision tree (DT) based on the model correlation coefficient and MAE. The proposed DNN outperformed other models with the highest correlation index, at 0.854, and the lowest MAE, at 1.5%. While [7] is the closest use case to our paper, it focuses exclusively on one process and does not seek to calculate the overall plant energy consumption. While they identify the potential of deep learning over traditional statistical and machine learning approaches, they do not evaluate the relative performance against other deep learning architectures. [PERSON] & [PERSON] [15] describe a platform, E-IoT, for collecting a wide range of data (over 1556 variables for one process) at a Korean manufacturing plant. From the data collected by E-IoT, they use a least absolute shrinkage and selection operator (LASSO) technique, based on machine learning, to extract relevant variables to predict plant-level STLF based on the first stage of one process, using LSTM. The proposed LSTM model achieved an MAE of 0.07 and an accuracy of 79%. The paper suffers from a significant lack of detail. For example, while the energy consumption profile for the process presented in [15] appears energy-intensive when compared to total plant energy consumption, it is unclear from the paper whether the manufacturing plant was energy-intensive or not. It is also unclear why only the first stage of the manufacturing process was used, and how many other processes are involved. Furthermore, no detail is provided on how the LSTM model configuration was selected, and it is not compared with existing techniques or other deep learning models. [PERSON] et al. [21] explore STLF for industrial customers in China and source data from a cable factory and a lithium factory located in Chongqing. They propose two short-term (20 days) energy consumption forecasting models using SVM and Random Forest based on historical consumption data as well as seasonal factors (holidays) and upstream value chain data i.e. the price of non-ferrous metals and raw material consumption at each factory. Both models accurately predicted the electricity loads for both factories. The MAPE for both the SVM and Random Forest model were similar for each factory - 5% for the cable factory and 2% for the lithium factory. The study highlighted the need for research using additional industry- and firm-specific variables to increase accuracy. As can be seen from the above, there is a paucity of research on demand-side STLF for energy-intensive manufacturing using deep learning and machine learning models. Decision making for utility companies has little in common with manufacturers. Similarly, STLF for residential and commercial use has little relevance to industrial use cases, and within industrial energy consumption, energy intensive manufacturing is idiosyncratic. The few similar studies lack detail on the degree to which they are energy-intensive manufacturers, aggregate all energy consumption, or focus on one process alone. Furthermore, where proposed deep learning and machine learning models were compared, they were either evaluated against only traditional techniques or only other deep learning models, or not at all. We addressed all of these shortcomings in our paper.