## 6 Conclusions and Future Work  
This paper is one of the first papers to compare the efficacy of deep-learning and machine-learning models for short-term load forecasting for energy-intensive manufacturing plants. In addition, we benchmark these models against the incumbent manual prediction technique and a classic time-series forecasting technique, ARIMA. Unlike existing studies, we consider multi-year ground truth data including total plant energy consumption data and data from two stages in a complex energy intensive manufacturing process. The use of production data contributed significantly to improving STLF accuracy by reducing the RMSE. Based on both the grid search results and Diebold-Mariano test results, we found that all the deep-learning and machine-learning models outperformed the incumbent manual technique. Furthermore, the GRU model (GRU-1-30) outperformed the basic RNN and LSTM models in RMSE (0.0305), MAPE (4.33%), and MAE (0.0305) in a very short inference time (0.7058 s). Accurate STLF can be used in a variety manufacturing processes to achieve energy efficiencies and can be used as an input in a range of operational decisions including energy management (e.g., heat storage and cooling), anomaly detection, predictive machine maintenance, and proactive plant and machine management, amongst others. The reduction of machine idle-times would seem to be particularly attractive to such manufacturers. Given the dearth of research on this topic in energy-intensive manufacturing, there are many avenues for future research. As the industrial Internet of things matures, a significantly larger volume of time-series data will be available to further refine the accuracy of the models and to extend the use of deep learning beyond prediction to actuation. For near real-time prediction, very short-term load forecasting (VSTLF) may be needed. In such use cases, rapid training times will be required. While GRUs may meet this criteria, further research is required. Furthermore, medium-term load forecasting may prove that fruitful, deep-learning training models may need to be augmented with historic trend data to account for longer seasonal cycles or predictable events. Medium-term load forecasting may enable new use cases including switches to more sustainable or lower-cost power supplies. Similarly, as production planning is prioritised over energy management in energy-intensive manufacturing, multi-step forecasting strategy may be more appropriate or preferable. This may require ensemble solutions and is worthy of exploration. This paper highlights the potential of deep learning and ARIMA in energy-intensive manufacturing. The adoption of deep learning, like all data science technologies, requires overcoming human, organisational, and technological challenges; however, against intense rivalry, firms may not have a choice. Conceptualization, A.M.N.C.R. and P.T.E. ; data curation, A.M.N.C.R. and P.R.X.d.C. ; Formal analysis, A.M.N.C.R., I.R.R., T.L. and P.T.E. ; investigation, A.M.N.C.R., P.R.X.d.C., I.R.R. and P.T.E. ; methodology, P.T.E. ; project administration, D.S. ; resources, T.L. ; validation, A.M.N.C.R. ; writing--original draft, A.M.N.C.R., T.L. and P.T.E. ; writing--review & editing, A.M.N.C.R., D.S., T.L. and P.T.E. All authors have read and agreed to the published version of the manuscript. This research has been partially financially supported by Fundacao de Amaparo a Ciencia e Tecnologia de Pernambuco (FACEPE), by the Irish Institute of Digital Business (IIDB) and dotLAB Brazil. The authors declare no conflict of interest. The following abbreviations were used in this manuscript: