A Guideline for Developing Time Series Forecasting Models to Predict Industrial Energy Demands
Year: 2026
Type: article
Abstract: Forecasting energy demand is crucial in industry to increase energy use efficiency and reduce greenhouse gas emissions. Although extensive research has been conducted in this field, it is still challenging for industrial companies to identify suitable methods for forecasting energy demand. Therefore, a new categorisation of energy forecasting models is developed in this paper. This categorisation is based on the available data and not on the methodology, as is usually the case. Thus, the intention is to make it easier to create a forecasting model and to identify the appropriate methodology. It also indicates what is needed to improve the forecast. The focus of this paper is on forecasting energy demand in industrial companies. To facilitate application, a guideline is established. The guideline describes which methodologies can be used based on the available data. The development of the guideline is based on various research projects for which forecasting models are created. The guideline starts with simple forecasting methods and gradually increases in complexity, including examples for each forecasting method. In addition, the methods available for each forecasting category are specified, and references are made to the relevant literature. After the description of the guideline, further explanation is provided on how the created forecasting model can be checked and integrated into a continuous improvement process.
Source: Energies
DOI: 10.3390/en19102328
Authors: Thomas Kurz, Vanessa Zawodnik, Cyrus Alexander Emami, Stefan Bohslavski, Thomas Kienberger
Institution: Montanuniversität Leoben
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G3127045006, G957178412
OpenAlex ID: W7160926000