Multi-criteria decision-making in the location selection for a solar PV power plant using AHP

Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA

In Turkey, current energy generations are not sufficient for the existing energy needs and besides, energy demand is expected to increase by 4–6 percent annually until 2023. Therefore, the government aims to increase the ratio of renewable energy resources (RES) in total installed capacity to 30 percent by 2023. By this date, total energy investments are expected to be approximately $110 billion. Turkey is the fastest growing energy market among the OECD countries. Therefore, Turkey is an attractive market for energy companies and investors. At this stage, site selection and deciding appropriate RES are the most important feasibility parameters for investment. In this study, “Site Selection in Turkey” issue for RES (solar, wind, hydroelectric, geothermal, biomass) is evaluated by the ELECTRE which is one of the Multi Criteria Decision Making (MCDM) methods. In addition, the reasons for choosing this method are explained according to the literature. The study emphasizes the importance of energy generation from renewable and sustainable sources and is concerned with improving the position of the country. The Turkish government offers many purchasing guarantees and high incentives, especially in the renewable energy sector. As a result of the analysis, the most suitable energy sources are presented according to the geography and energy potential of the regions. The study aims to inform energy firms and everyone related with RES about Turkey’s RES opportunities.

Turkey’s 2023 Energy Strategies and Investment Opportunities for Renewable Energy Sources: Site Selection Based on ELECTRE

Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil

A geographical information system-based multi-criteria method for the evaluation of solar farms locations: A case study in Souss-Massa area, southern Morocco

Primary energy sources are running out due to the increase in electrical energy consumption. Environmental problems caused by primary energy sources are also increasing. Using more renewable energy resources (RES) can be considered as one of the most powerful solutions to address these problems. Today, required photovoltaic power systems (PVPS) and wind energy systems (WES) are widely used as RES for addressing these problems. Because of their high costs, feasibility studies are required for locating large systems associated with these resources. In this study, various suggestions are determined about location selection, which is an important stage in the PVPS’s establishment. Hence, the criteria for selecting the appropriate location are analyzed by the multicriteria decision making (MCDM) methods and the results are evaluated for 5 cities in the Central Anatolian Region of Turkey. In conclusion, it is determined which city is the most suitable place for installation of solar power plants.

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Optimal Site Selection for a Solar Power Plant in the Central Anatolian Region of Turkey

Technology is developing day by day in the world. In addition, developing technologies bring innovations and conveniences to all areas of life. However, ensuring the continuity of these innovations brought by technology reveals different problems. Smart home approach, which increases the quality of human living areas, is one of the most popular working subjects of recent times. In a smart home, built with the Internet of Things technology products, it is very important that the sensors and control devices communicate in a safer way and work in a coordinated manner to ensure the ecosystem's continuity in a safer way. In this study, an IoT based smart home testbed was realized by using MQTT communication protocol which one of the most used IoT communication protocols. With the developed system, the control of the smart home and the operating performance of the system were controlled with the mobile application. The results obtained in the light of the data provided by the test show that the system developed with the MQTT communication protocol can successfully ensure data flow and control in Smart Home applications.

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IoT based Smart Home Testbed using MQTT Communication Protocol

Solar power prediction is an important problem that has gained significant attention in recent years due to the increasing demand for renewable energy sources. In this paper, we present the results of using four different regression models for solar power prediction: linear regression, logistic regression, Lasso regression, and elastic regression. Our results show that all four models are able to accurately predict solar power, but Lasso regression and elastic regression outperform linear and logistic regression in terms of predicting the maximum solar power output. We also discuss the advantages and disadvantages of each model in the context of solar power prediction.

Solar Power Prediction using Regression Models

Bu calismada, Musiad'in Ahiler Ajansi ile ortak yuruttugu proje kapsaminda, Kirikkale Universitesi kampus alaninda bulunan ve ruzgar hizi belirlenen bir bolgenin verileri kullanilarak, bolgenin ruzgar enerjisi potansiyeli analiz edilmistir. Calismada ilk olarak ruzgar enerjisiyle alakali kavramlar aciklanmis, daha sonra belirlenen ruzgar verisi yardimiyla bolgede kurulabilecek 800 kW'lik anma gucune sahip Enercon-53 ruzgar turbinin farkli kule yukseklikleri icin enerji analizi ve maliyet analizi yapilmistir. Bu analiz yardimiyla kule yuksekliginin enerji uretim miktarina, kapasite faktorune ve yatirimin amorti edilebilecegi sureye etkisi incelenmistir.

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Kırıkkale Üniversitesi'ne Kurulacak Olan Rüzgar Türbini İçin Enerji ve Maliyet Analizinin Yapılması

Due to the non-storable nature of electric energy, short-term and long-term electricity generation and consumption forecast are critical to keeping electricity market in balance. In addition, the production estimate of wind energy is parallel to the estimate of wind speed. Since wind speed forecasts includes seasonal and time-dependent trends, time series forecasting methods produce successful results in wind energy forecasting. However, choosing the most appropriate time series forecasting method for short-term and long-term production forecasts is of special importance. In this study, short-term and long-term wind speed estimations were made for the wind turbine at Kirikkale University by using Exponential Smoothing (ES) and ARMA (Auto Regressive Moving Average) methods. The most suitable methods for forecasting short-term and long-term wind speed have been determined with the obtained results.

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Mustafa Yasin Erten

Papers

Optimal Site Selection for a Solar Power Plant in the Central Anatolian Region of Turkey

IoT based Smart Home Testbed using MQTT Communication Protocol

Solar Power Prediction using Regression Models

Kırıkkale Üniversitesi'ne Kurulacak Olan Rüzgar Türbini İçin Enerji ve Maliyet Analizinin Yapılması

Comparing time series forecasting methods to estimate wind speed in kirikkale region