Sosyal medya platformları, kullanıcılara tüm kategorilerde düşüncelerini ve duygularını paylaşacakları bir mecra oluşturmuş ve günlük rutinin bir parçası haline gelmiştir. Yaygın sosyal medya kullanımı ile devasa boyutlarda veriler oluşmaya başlamıştır. Veri hacmindeki bu hızlı artışla, bu verilerin yönetilme ve içerisinden anlamlı bilgi çıkarılması ihtiyacı doğmuş ve akıllı hesaplama yöntemlerinin bu verileri analiz etmesi son derece kritik bir hale gelmiştir. Duygu analizi, anlamlı bilgi elde etmek için verilere uygulanan süreçler bütünüdür. Bu makalede, kullanıcıların durum güncellemelerini “tweet” şeklinde yayınladığı, çok sayıda kullanıcı tarafından kullanılan popüler sosyal medya sitesi olan Twitter verileri üzerinde duygu analizi yapılmıştır. Bu analizi gerçekleştirmek için Naïve Bayes ve Support Vector Machine gibi makine öğrenme yöntemleri kullanılarak tweetleri pozitif ve negatif sınıflara ayırmak için akıllı bir model oluşturulmuş ve karşılaştırmalı sonuçlar verilmiştir

Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).

Photovoltaic (PV) energy systems are one of the most significant renewable resources, requiring efficient solutions for solar power generation and maintenance. Accurately predicting PV energy generation and solar radiation is essential for managing grid maintenance and making energy market decisions. This study proposes prediction models based on the use of deep learning, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, to fill the gap in big data analysis as more renewable energy data is collected. These models will be compared with traditional machine learning methods including Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Data from the GAPYENEV Centre at the University of Harran is used to implement predictive models. Several prediction error metrics such as MAE, MSE, RMSE, R2 and accuracy are used to evaluate the predictive ability of the models.