Dr. Liljana Ferbar Tratar, Full Professor, Department of Mathematics, Statistics and Operations Research, School of Economics and Business, University of Ljubljana (SEB LU), Slovenia

Biography: Prof. Liljana Ferbar Tratar received her PhD in 1998 in the field of operations research at the School of Economics and Business, University of Ljubljana in Slovenia. In 1995 she received her master's degree at the SEB LU, Slovenia, and in 2001 at the Faculty of Mathematics, University of Zagreb, Croatia. She graduated in 1993 at the Faculty of Mathematics, University of Ljubljana, the profile of a graduate engineer of mathematics. Since 1993 she has worked at the School of Economics and Business in Ljubljana in the Department of Mathematics, Statistics and Operational Research. Her area of scientific interest is operations research, especially inventory management and demand forecasting. She has published scientific papers in several international scientific journals in the field of operations research and optimization.

Speech Title: Forecasting Methods in Engineering

Abstract: Forecasting in engineering is one of the most important topics when it comes to optimization, which is related to energy savings, material savings, increasing efficiency, appropriate and correct decisions at the level of a company, institution, city or region. Moreover, forecasting is indirectly related to cost savings and 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 is forecasting more challenging. Forecasting errors are usually penalized drastically. However, well-developed forecasting approach represents a competitive advantage, therefore a company may significantly reduce expenditure and increase a profit. Many publications on forecasting have appeared during the past years. Long-term forecasting methods offer many opportunities for the strategic planning and the optimal scheduling, whereas short-term forecasting approach would help to reach the optimal daily operations and the maximum utilization of the company's resources. Although different forecasting techniques can be used, the major conclusions are that exponential smoothing methods are 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 we analyse the forecasting performance of Additive, Multiplicative and Extended Holt-Winters method. We also analyse whether the data format influences the choice of forecasting method: is the most accurate method for monthly data also the best method for quarterly data?

Keywords: Forecasting, exponential smoothing methods, Holt-Winters method, optimization, energy