Kübra TÜMAY ATEŞ

Çok Katmanlı Yapay Sinir Ağı Modeli ve Kültürel Algoritma Modeli Kullanılarak Geliştirilen Melez Yöntem ile Kısa Vadeli Fotovoltaik Enerji Santrali Çıkış Gücü Tahmini

Güneş enerjisinden elde edilen sürdürülebilir enerji, konut, ticari ve endüstriyel uygulamalarda enerji talebini karşılamada önemli kaynaklardan biri haline gelmiştir. Ancak güneşten elektrik enerjisi üretimindeki temel zorluk, fotovoltaik enerji santrallerinde hava koşullarından kaynaklanan güçteki anlık değişimlerdir. Büyük ölçekli güneş FV enerji santralleri için, güç dengesizlikleri elektriksel olarak sistem verimliliği ve kararlılığını olumsuz yönde etkilemektedir. Bu nedenle, FV enerji santrallerinin çıkış gücünün kısa vadede doğru tahmin edilmesi, elektrik şebekesi üretim, dağıtım ve depolamanın günlük/saatlik verimli yönetimi ve enerji piyasasında karar verme için büyük önem taşımaktadır. Bu makalede, FV enerji santralinin güç üretimini tahmin etmek için kültürel geçiş hedefi temelinde popülasyon tabanlı bir algoritma geliştirmeyi amaçlamaktadır. Aynı zamanda, her yinelemede tüm değişkenleri göz önünde bulundurarak daha hızlı yakınsamaya olanak sağlaması özelliği ile Parçacık Sürü Optimizasyon (PSO) yöntemi ile kısa vadeli tahmin yapılmaktadır. Kısa vadeli FV panel çıkış güç tahminin sonuçlarını en az hata oranı ile elde etmek için çok katmanlı yapay sinir ağı modeli PSO ve Kültürel Algoritma (KA) ile kullanılarak melez yöntem oluşturulmuştur. KA iterasyon sırasında toplanan bilgileri depolama ve daha sonra kullanma özelliği ile evrimsel algoritmalardan daha hızlı yakınsama sağladığı için FV enerji çıkış gücü kısa vadeli tahmininden etkin sonuçlar elde edilmiştir.

Anahtar Kelimeler:

Fotovoltaik enerji, Kültürel algoritma, Parçacık sürü optimizasyonu, Kısa vadeli güç tahmini, Melez yöntem

Short Term Photovoltaic Power Plant Output Power Forecasting with Hybrid Method Developed Using Multilayer Artificial Neural Network Model and Cultural Algorithm Model

Sustainable energy obtained from solar energy has become one of the important sources in meeting the energy demand in residential, commercial and industrial applications. However, the main difficulty in generating electricity from solar energy is the instantaneous changes in power caused by weather conditions in photovoltaic (PV) power plants. For large-scale solar PV power plants, power imbalances electrically negatively affect system efficiency and stability. Therefore, accurate forecasting of the output power of PV power plants in the short term is of great importance for efficient daily/hourly management of electricity grid generation, distribution and storage and for decision making in the energy market. This paper aims to develop a population-based algorithm estimating the power generation of a PV power plant based on the cultural transition target. Also, short-term forecasting is carried out based on the Particle Swarm Optimization (PSO) method, with the feature of allowing faster convergence by considering all the variables in each iteration. In order to obtain the results of short-term PV panel output power estimation with the least error rate, a hybrid method was created by using the multilayer artificial neural network model with PSO and Cultural Algorithm (CA). Since the CA provides faster convergence than evolutionary algorithms with the ability to store and use the information collected during iteration, effective results are obtained from the short-term forecasting of the PV energy output power.

Keywords:

Photovoltaic energy, Cultural algorithm, Particle swarm optimization,

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