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A Forecasting Study for Renewable Energy Resources Investments in Turkey: TOPSIS-Based Linear Programming Model

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Abstract

In this paper, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)–based linear programming (LP) approach is developed for determining optimal quantities of renewable energy sources according to the source’s potential compared with other renewable energy alternatives. A combined model is established with the forecasting model, TOPSIS, and LP methods to determine optimal results. The developed decision support approach’s applicability is illustrated in this paper. The paper illustrates how renewable energy sources can be preferred to meet additional energy demand in the next five years in Turkey using the developed TOPSIS integrated LP model. For this objective, a time series analysis is applied to estimate production capacity for the constraints of the mathematical model. Then, a question is answered how much energy should be produced from which alternative for each year? A flexible structured mathematical model is developed to meet the necessary energy requirements in dynamic and variable economic systems. So, the decision-maker can assign different criteria weights in the TOPSIS model, which is integrated with LP, and obtain new optimal solutions related to requirements in the future.

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Authors and Affiliations

  1. Department of Industrial Engineering, Baskent University, Ankara, 06810, Turkey

    Dicle Oluklu & Yusuf Tansel İç

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  1. Dicle Oluklu
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  2. Yusuf Tansel İç
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Correspondence to Yusuf Tansel İç.

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Appendices

Appendix 1

Table 10 Renewable energy production for Turkey (GWh)
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Appendix 2

Fig. 8
figure 8

Geothermal Production Forecast

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Appendix 3

Fig. 9
figure 9

Hydroelectric Production Forecast

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Appendix 4

Fig. 10
figure 10

Solar Production Forecast

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Appendix 5

Fig. 11
figure 11

Wind Production Forecast

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Oluklu, D., İç, Y.T. A Forecasting Study for Renewable Energy Resources Investments in Turkey: TOPSIS-Based Linear Programming Model. Process Integr Optim Sustain 7, 559–573 (2023). https://doi.org/10.1007/s41660-022-00297-5

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