## 1 Introduction  
The industrial sector is the largest consumer of delivered energy worldwide, and energy-intensive manufacturing is the largest component in that sector [1]. Energy-intensive manufacturing includes the manufacture of food, beverage, and tobacco products; pulp and paper; basic chemicals; refining; iron and steel; nonferrous metals, and nonmetallic metals [2]. Energy-intensity is driven by the mix of activity in these sectors including basic chemical feed-stocks; process (including heating and cooling) and assembly; steam and cogeneration; and building-related energy consumption, e.g., lighting, heating, and air conditioning [2]. World industrial energy consumption is forecasted to grow from c. 242 quadrillion British thermal units (Btu) in 2018 to about 315 quadrillion Btu in 2050;the proportion of energy-intensive manufacturing is forecasted to remain at approx. 50% during that period [1]. It is widely held that energy consumption tends to be positively associated with a higher rate of economic growth [3]; however, against this backdrop is the environmental impact of this consumption. As part of the United Nations Sustainable Development Goals (SDG), SDG 9 has industrial targets to reduce carbon dioxide (CO\({}_{2}\)) emission per unit of value added through increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes [4]. To meet these targets, governments worldwide are imposing regulations and taxes to reduce the environmental impact of industrial energy consumption. Firm-level forecasts of energy consumption are essential for precautionary and mitigation measures decision-making by management to minimise environmental impact, to manage cash flow, and to reduce or eliminate risk [5]. Accurate firm-level monitoring can also improve the data quality available to policymakers for local, regional, and national policies and actions. Extant literature has typically (i) focused on supply-side perspectives, (ii) aggregated energy costs, and (iii) failed to recognise the idiosyncrasies of the energy-intensive manufacturing sector and the associated centrality of energy management in production planning. There is a paucity of studies in demand-side process-related short-term load forecasting (STLF) using deep learning and machine learning for energy-intensive manufacturing. The limited studies that have been published do not compare deep-learning performance against widely used machine-learning models, classical time series models, or approaches used in practice. In addition to proposing prediction models, we also address this gap. In this paper, we focus on performance analyses of deep-learning and machine-learning models for firm-level STLF considering the energy-intensive manufacturing process of a thermoplastic resin manufacturer. We use energy consumption and production flow data from a real Brazilian industrial site. In addition to the energy consumption time series, we use data from two different stages of the thermoplastic resin production process: polymerisation and solid-state polymerisation. We propose three deep-learning models--simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU)--and two machine-learning models--Support Vector Regression (SVR) and Random Forest--for predicting daily energy consumption. These techniques were selected as they are referenced in the literature on STLF for demand-side energy consumption [6; 7]. We use the grid search method to identify the best model configurations. We compare the performance of the deep-learning and machine-learning models against (i) a classical time series model, Autoregressive Integrated Moving Average (ARIMA), and (ii) the current manual approach at the case site. The remainder of this paper is organised as follows. Section 2 presets the description of the data, preprocessing, and evaluation metrics used in our work. Section 3 presents the models identified for evaluation.