Güneş enerji santrallerinde makine öğrenmesi algoritmaları ve coğrafi bilgi verileri kullanılarak enerji üretiminin tahminlenmesi
Prediction of energy production in solar power plants usi̇ng machine learning algorithms and geographic information data
Yazar: EREN POLATCAN
Danışman: PROF. DR. HAYRİ HAKAN DENLİ
İstanbul Teknik Üniversitesi / Bilişim Enstitüsü / Bilişim Uygulamaları Ana Bilim Dalı / Coğrafi Bilgi Teknolojileri Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Mühendislik Bilimleri = Engineering Sciences

Özet:
Yenilenebilir enerji, fosil yakıt enerji kaynakları kullanımını ve bu tür yakıtların kullanımı sonucu ortaya çıkan çevre kirliliğini ve dolayısıyla küresel iklim değişimini en aza indirmek adına, başta Avrupa olmak üzere bütün dünya çapında umut verici teknolojilerin en önemlilerinden birisi olarak kabul edilmektedir. Bu bağlamda dünya genelindeki yenilenebilir enerji santralleri sayısı günden güne artmaktadır. Dünyadaki yenilenebilir enerji kaynakları içerisinden en sık kullanılanlardan birisi güneş enerjisidir. Güneş enerjisinden yüksek verim elde edebilmek için, kurulacak santrallerin planlaması ve arazi tahsisi çok önemlidir. Çünkü başta güneş ışınlarının yoğunluğu ve güneşlenme süresi olmak üzere, güneşten yüksek verimde elektrik elde edebilmek için gerekli meteorolojik koşullar coğrafi konumlara göre farklılık göstermektedir. Meteorolojik koşulların haricinde arazinin fiziksel koşullarının da santral kurulumuna uygun seçilmesi, güneş panellerinin güneşten maksimum miktarda ışık almasını ve gölgelenmeden korunmasını sağlamak için önemlidir. Ayrıca kurulu santrallerden ne kadar elektrik üretilebileceğini yaklaşık olarak bilmek, termik kaynak kullanımının planlamasında büyük rol oynar. Güneş enerji santrallerinden enerji üretimi; güneş ışınlarının geliş açısı, sıcaklık, coğrafi konum, gölgeleme, hava kirliliği ve yağış gibi coğrafi bilgi katmanlarına bağlı değişkenlik göstermektedir. 
Bu tez çalışması ile güneş enerjisi üretimine etki eden coğrafi bilgi katmanlarının dinamik olarak işlendiği bir matematiksel model geliştirilmiş, gerçekleştirilen enerji üretimi tahminlenmesi ile santral sahiplerine, dağıtım şirketlerine ve resmi kurumlara referans bilgilerin sunulması amaçlanmıştır. Modelde, makine öğrenmesi ve zaman serisi algoritmaları kullanılarak, yüksek başarı oranı içeren tahminlerin yapılması amaçlanmış ve yapay zeka teknikleri kullanmanın geleneksel aritmetik hesaplamalara göre tahmin başarı oranı açısından farkı da incelenmiştir. 
Bu çalışma ile, tasarımı ve demo modeli oluşturulan coğrafi bilgi sistemi sayesinde yapılacak güneş enerjisi üretim tahmini sonucu, hem mevcut hem de yeni güneş enerjisi santralleri için daha hassas değerlendirmelerin yapılmasına ve yatırımların geri dönüşünün arttırılabilmesine olanak tanıyacaktır. Geliştirilen makine öğrenimi tabanlı modelde yer alacak coğrafi katmanların haricindeki panel ve insan kaynaklı üretime etki eden faktörler ise, güneş santrallerinin kapasite ve üretim izlemesini yapan Scada sistemlerindeki veriler kullanılarak hesaplanmaktadır. 
Makine öğrenmesi tabanlı tahmin modeli için yazılım geliştirme, sunucu ve operasyonel maliyetlerin hesaplanması için gerçekleştirilen fizibilite çalışması da böyle bir model oluşturmanın ve kullanmanın tahmini maliyet değerlerini ortaya koymuştur. Ayrıca santrallerde güneş takip sistemi olup olmaması durumu da göz önünde bulundurulmuş ve güneş takip sistemi dahiliyeti sonucu oluşacak üretim tahmini de hesaplanmıştır. Sonuç olarak güneş enerjisi santrallerinde üretim tahmini yaparken coğrafi bilgi verileri ve yapay zeka modelleri kullanıyor olmanın geleneksel hesaplama yöntemlerine göre daha başarılı sonuçlar verdiği görülmüştür.

Abstract:
Renewable energy is considered one of the most promising technologies worldwide, especially in Europe, to reduce the use of fossil fuel energy sources and the environmental pollution and the effect to the global climate change resulting from the use of such fuels. In this context, the number of renewable energy plants worldwide is increasing day by day. One of the most frequently used renewable energy sources in the world is solar energy. For high efficiency from solar energy, planning and land allocation of the plants to be built are very important. In addition, by knowing approximately how much electricity can be generated from the installed plants plays a major role in the planning of thermal energy use. 
The main objective of this study is to identify the geographic information layers that effect solar energy production by examining the economic and environmental benefits of using solar energy. Another objective of the study is to calculate the energy production of a specified solar power plant using artificial intelligence techniques and geographic information data. The study also mentions the areas of application of artificial intelligence and states that it is an important tool for improving existing processes.
Energy production from solar power plants varies depending on geographic information layers such as the angle of incidence of solar rays, temperature, geographical location, shading, air pollution, and rainfall. In the study, a mathematical model has been developed by dynamically processing the geographic information layers effecting solar energy production, and to provide reference information to power plant owners, distribution companies, and official institutions by predicting the energy production to be realized. In the model, it has been aimed to make high success rate predictions using machine learning and time series algorithms, and the difference between using artificial intelligence techniques and traditional arithmetic calculations in terms of prediction success rate has been also investigated. 
In this study, a mathematical model analysis has been conducted in which geographical layers effecting solar energy production were dynamically processed. The model aims to make high-accuracy predictions using machine learning and time series algorithms. In the study, the prototype of the model has also been developed and production forecasting has been performed over a sample case, and the effects of geographical layers with high weight in energy production in solar power plants were observed on production forecasts.
The predictions in the 'time series prediction model', developed using the Python programming language during the study, has been made with the Facebook's open source Prophet library. The hourly one year energy production data from the Afta/Karapınar (a province of Konya), power plant and the geographical data as temperature, sunshine duration, precipitation amount and the average daily values of the angle of incidence of solar rays which have the highest impact on electricity production from solar energy has been used an input data in the model. Thanks to the Prophet time series prediction application based on machine learning, thousands of input data from the 2023 year has been analyzed automatically in seconds and the model has been workout. While the model is running, a prediction method of the hourly energy production for the coming 10 days period, and by adding new data every coming day, has been developed.
The study evaluated various time series forecasting models from different libraries and ultimately utilized Facebook Prophet, an open-source forecasting software developed by Meta. ARIMA and Exponential Smoothing models were observed to be less successful than Prophet in capturing the relationships between multiple parameters. The meteorological geographic information data used in the model is defined as irregular data and can change continuously over time. Prophet is specifically designed to train data that lacks a specific pattern or structure, making it a more suitable tool for forecasting time series data with strong seasonal patterns, such as daily or monthly data.
Machine learning is a concept intertwined with artificial intelligence. Traditional rule-based algorithms used in traditional software are used together with statistical algorithms in machine learning and artificial intelligence applications to enable computers to perform capabilities such as decision-making and problem solving. In this study, the positive effect of using artificial intelligence algorithms on production forecasts has been observed. In current applications, production forecasts for solar power plants are mostly made using rule-based algorithms with arithmetic operations. Because it is not yet in practical use because it is both a new technology and the initial investment costs of the system to be developed are high to be able to develop a high-success prediction model by analyzing millions of data. However, it is expected that the process of production forecasting with artificial intelligence in the energy sector, as in traffic, weather, and the supply, production, etc. processes of large companies, will become widespread in the near future. The important thing is to be able to train artificial intelligence correctly and to choose the most appropriate prediction model for the case to be worked on.
The forecast is made based on the main layers effecting solar energy production; data on the angle of incidence of solar rays, temperature, geographic location, shading, air pollution, and precipitation. In addition, factors effecting production from panels and humans are calculated using the actual value data stored in the scada systems that monitor the capacity and production of solar power plants. The development of the model will allow for a more accurate estimation of the potential of solar energy production. This will allow for more precise evaluations for both existing and new solar power plants and increase the return on investment.
In the Feasibility section of the study, the initial investment and regular operational effort and costs required to develop such a prediction model were estimated based on the pricing of global cloud service providers and developer companies.
The development of the model will also make a significant contribution to the planning and allocation of land for solar energy production. In addition, it will help to reduce fossil fuel use and prevent environmental pollution by making solar energy production more efficient. Adaptation of the model to different countries and climatic conditions is also an important issue for future research. These adaptations will allow the model to be used in a wider range. Since the geographic information system to be created to estimate solar energy production will have a cloud-based software infrastructure, it can become a software that can be used in many countries after the necessary configurations are made.