Novel short term solar irradiance forecasting models Emre Akarslan a, * , Fatih Onur Hocaoglu a , Rifat Edizkan b a Afyon Kocatepe University, Department of Electrical Engineering, Turkey b Eskis ¸ehir Osmangazi University, Department of Electrical and Electronics Engineering, Turkey article info Article history: Available online 10 February 2018 Keywords: Solar irradiance Forecasting Semi-empiric models Angstrom-prescott equations abstract The Angstrom-Prescott (A-P) type models are widely used for solar irradiance forecasting. These models use the sunshine duration and extraterrestrial irradiance values. The accuracies of the A-P models are highly region dependent coefficients. Therefore, these coefficients are determined empirically. In this study, five novel semi-empiric models for hourly solar radiation forecasting are developed. These models utilize historical data of the solar irradiance, the extraterrestrial irradiance and the clearness index while forecasting. To test the effectiveness of the proposed models, three different regions are deliberately selected, and solar data are measured and collected hourly. To show the effectiveness of the proposed models, the forecasting results are compared with the A-P type equation based models. The proposed approach is concluded to be superior compared with the previously developed A-P type equation based models. © 2018 Published by Elsevier Ltd. 1. Introduction As the development of global economy continues, fossil fuels such as oil and coal are being depleted, environmental pollution and greenhouse effects are continuing to increase, and the energy crisis and environmental protection are becoming the focal points of sustainable development [1]. Due to the global warming and depletion of the conventional fuel sources, the interest on using clean and inexhaustible energy sources is growing. Among those sources, solar energy seems to be one of the promising options. Solar energy is a sustainable energy resource that can be consid- ered an efficient alternative to fossil fuels [2] and critical to meet our energy needs [3]. Use of this untapped energy resource results in reducing carbon emissions and decreasing the economic and supply risks related to a reliance on fuels [4]. With a rapid reduction in cost in the last five years and having reached grid parity in various countries, solar photovoltaic (PV) electricity is poised to be one of the major future energy sources. However, the instantaneous power output of a PV system can vary substantially depending on local meteorological conditions and system’s performance, which makes it an intermittent type of renewable energy with high variability [5]. Undoubtedly, accurate estimation of the solar radiation is an essential consideration for the optimal design of a solar system. Utilization of a precise model is of vital importance for areas with specific solar potential. Furthermore to reduce grid- integration costs, accurate and certain solar energy forecasts are required [6]. Therefore, developing accurate models for the estimation of solar radiation from available data is of great importance [4]. There are several methods used for solar radiation forecasting. Among them, intelligent methods are one of the most popular ones [7]. reported a new fuzzy model to forecast daily global solar irradiation at ground level. In their method, Fuzzy c-means clustering is used to establish the membership functions while the overall algorithm is developed in the frame of functional fuzzy systems. It is concluded that the model accuracy is adequate for routine practical purposes [8]. proposed a novel hybrid (Mycielski- Markov) model for hourly solar radiation forecasting. The model searches the longest repeated pattern in the past and according to the pattern, a prediction is made according to this pattern. To model the probabilistic relations of the data, a Markov chain model is adopted; by this way the historical search by the model is strengthened [9]. proposed a new hybrid technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. They proposed three different models and compared their performances [10]. used different kinds of ANN models (Delay based, Neuron based, Activation based, Multi-parameter based) to * Corresponding author. Afyon Kocatepe University, Department of Electrical Engineering; Turkey.d. E-mail addresses: e.akarslan@gmail.com (E. Akarslan), fohocaoglu@gmail.com (F.O. Hocaoglu), redizkan@ogu.edu.tr (R. Edizkan). Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene https://doi.org/10.1016/j.renene.2018.02.048 0960-1481/© 2018 Published by Elsevier Ltd. Renewable Energy 123 (2018) 58e66