Forecasting methods in engineering

Abstract

Forecasting in engineering is one of the most important topics when it comes to optimisation, which is related to energy savings, material savings, increasing efficiency, and appropriate and correct decisions at the level of a company, institution, city, or region. Moreover, forecasting is indirectly related to cost savings and the sustainable development of society and environment. In the energy industry (electricity, natural gas, heat load), there are requirements to balance the supply and demand. Markets are very dynamic, and for this reason forecasting is more challenging. Forecasting errors are usually penalized drastically. However, a well-developed forecasting approach represents a competitive advantage, and so a company may significantly reduce expenditure and increase profits. Many publications on forecasting have been published during recent years. Long-term forecasting methods offer many opportunities for strategic planning and optimal scheduling, whereas a short-term forecasting approach would help attain optimal daily operations and the maximum utilisation of the company's resources. Although different forecasting techniques can be used, the major conclusions are that exponential smoothing methods are the simplest and the least expensive. Distinguished by their simplicity, their forecasts are comparable to forecasts of more complex statistical time series models. In this paper, the forecasting performance of Additive, Multiplicative, and Extended Holt-Winters methods were analysed. We also analyse whether the data format influences the choice of the forecasting method: is the most accurate method for monthly data also the best method for quarterly data?