Comprehensive Energy Resource Management for Essential Reduction of the Total Cost

The comprehensive energy resource management presents kind of global optimisation, which is much more cost-effective than separate treatment of energy-demand forecasting and energy-inventory data. In this paper, two-stage supply chain is presented, where distributor of derivate energy products (e.g. hot water, steam, electricity) shares information with its supplier of primary energy sources (e.g. coal, crude oil, gas). Different methods of energy-demand forecasting can be used, although exponential smoothing methods are most often used in practice because they are simple, fast and inexpensive. In this study we analysed a modified Holt-Winters (HW) method and we describe a method for simultaneous optimisation of forecasting method and a stock control policy. We compared the proposed joint optimisation of total cost with the optimisation, based solely on forecasting data. The total cost presents the joint cost of distributor and supplier. The data consisted of 756 quarterly real series from M3-Competition and 300 simulated demand patterns. We have shown that the total cost can be reduced dramatically if we use the joint optimisation instead of separate treatment of forecasting method and inventory model. We obtained the best result with the modified HW method; the essential reduction of total cost was reached in case of simulated data as well as in case of real data.

[1]  Liljana Ferbar Tratar,et al.  The comparison of Holt–Winters method and Multiple regression method: A case study , 2016 .

[2]  Yungao Ma,et al.  The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques , 2013 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[5]  I. Palcic,et al.  Project Evaluation Using Cost-Time Investment Simulation , 2014 .

[6]  Branimir Pavković,et al.  Modelling, Simulation and Optimization of smalscalle CCHP energy systems , 2015 .

[7]  Bojan Lalic,et al.  Cost-Time Profile Simulation for Job Shop Scheduling Decisions , 2013 .

[8]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[9]  Edvard Govekar,et al.  Linear and Neural Network-based Models for Short-Term Heat Load Forecasting , 2015 .

[10]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[11]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[12]  John E. Boylan,et al.  On the interaction between forecasting and stock control: The case of non-stationary demand , 2011 .

[13]  David Escuín,et al.  On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry , 2017, J. Comput. Appl. Math..

[14]  Liljana Ferbar Tratar Joint optimisation of demand forecasting and stock control parameters , 2010 .

[15]  Damir Šljivac,et al.  Cost-benefit comparison of on-grid photovoltaic systems in Pannonian parts of Croatia and Serbia , 2014 .

[16]  Enriqueta Vercher,et al.  A decision support system methodology for forecasting of time series based on soft computing , 2006, Comput. Stat. Data Anal..

[17]  Xiande Zhao,et al.  Freezing the master production schedule for material requirements planning systems under demand uncertainty , 1993 .