## 5 Related Work  
Short term load forecasting using deep learning and machine learning has been examined from a variety of perspectives. For example, there is a well-established literature on supply-side energy consumption and demand forecasting using deep learning from the perspective of the management and optimisation of power systems and electricity grids. These include studies using deep neural networks [6], deep belief networks [51], CNNs [52], Autoencoder and LSTM [53], SVM and Random Forest [21] amongst others. The focus of this paper is demand-side. STLF for grids and utility companies have a fundamentally different motivation and context than manufacturing firms, not least the public interest aspect of energy systems, as opposed to profit maximisation, operational efficiencies and other business objectives. Similarly, there has been a number of studies on the use of deep learning for forecasting load prediction for different energy consumer types--residential [19; 22; 54], commercial [55; 56], and industrial [11; 57]. While there are certainly new knowledge generated by these works, their focus is overwhelmingly on load forecasting for utility companies and grids. Residential and commercial use cases have fundamentally different energy consumption patterns than industry in terms of decision-making time horizon, building code standards, population density, building design, and response to regional climate, amongst others [58]. As discussed in Section 1, energy-intensive manufacturing has a significantly different energy consumption profile than other industry sectors, leaving aside the obvious differences with residential and commercial use. As a result, the motivation for load forecasting is substantially different than other industrial use cases. In particular, these operations tend to have high process-related energy requirements, are not subject to climate changes, and energy management is core capability in their business. As production is central to manufacturing, the demand for energy is derived from production planning and energy forecasting and optimisation based on the over-riding demands of production [59]. [PERSON] et al. [6] explore demand-side STLF for variety of industry categories including manufacturing. Using data sourced from a Korean utility company, they propose a deep neural network (DNN) based STLF framework based on industry category, temporal patterns, location and weather conditions. A comparative analysis was performed with three different forecasting techniques--shallow neural network (SNN), double seasonal Holt-Winters (DSHW) and ARIMA. Using MAPE and relative root mean squared error (RRMSE). The results suggest that the DNN-based STLF model achieved the best performance when compared to the other models with lower MAPE (2.19%) and lower RRMSE (2.76%). Our approach differs in three important ways. Firstly, we adopt RNNs that have significant advantages in terms of time series data. Second, in [6], because the data comes from the power company, all energy consumption at a firm level is bundled up and it is not possible to distinguish different sources of energy consumption and their impact from within the firm e.g., buildings vs. process-related consumption. Third, we focus on energy-intensive manufacturing. It is not clear whether energy-intensive manufacturing is included in [6]. [PERSON] [60] explore the use of a deep feedforward neural networks (DFNN) and a deep RNN (DRNN) to predict STLF for a medium-sized manufacturing facility. Inputs to the DNNs included weather conditions and machining schedules. Results suggest that both models performed well but that the DRNN outperformed the DFNN for predictions of building energy consumption, achieving an accuracy of 96.8% compared to 92.4% for DFNN. The focus of [60] was optimising heating, ventilation and air conditioning (HVAC). As such, the impact of the production process was not a focus _per se_.