This paper presents a method for predicting the energy yield of a photovoltaic (PV) system based on the ARIMA algorithm. We analyze two key time series: the specific yield and the total yield of the PV system. Two ARIMA models are developed for each time series: one selected by the authors and one determined by SPSS. Model performance is evaluated through fit statistics, providing a comprehensive assessment of model accuracy. The residuals’ ACF and PACF are examined to ensure model adequacy, and confidence intervals are calculated for residuals to validate the models. A monthly forecast is then generated for both time series, complete with confidence intervals, to demonstrate the models’ predictive capabilities. The results highlight the effectiveness of ARIMA in forecasting PV energy yields, offering valuable insights for optimizing PV system performance and planning. This study contributes to the field of renewable energy forecasting by demonstrating the applicability of ARIMA models in predicting the monthly performance of photovoltaic systems.

Molecular docking algorithms predict the binding of a macromolecule (receptor) and a small molecule (ligand). Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Presuming adequate search strategies, a scoring function’s location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. In this paper a survey of scoring functions for protein-ligand docking is presented.

The maximum network flow problem is a fundamental problem in network flow theory. The Ford-Fulkerson and Edmonds-Karp algorithm are popular optimization algorithms for computing the network flow. The current paper presents a C# implementation of Ford-Fulkerson and Edmonds-Karp algorithms for solving the maximum flow problem in networking.

In recent years, a great popularization of the production of electricity from photovoltaic systems has been observed, which gives hope for reducing environmental pollution and tackling the shortage of natural resources used for electricity generation. This article will focus on studying the effect of energy produced by a grid-connected rooftop solar photovoltaic system for small households.

The interest in the renewable energy sources is increasing due to the depletion of the conventional energy sources and the environmental pollution. In this paper we present a computer simulation and investigations of the roof mounted photovoltaic system. The results of the generated power of the 5kW built-in photovoltaic system by months over a period of 5 years are presented. Depending on the meteorological conditions, the investigated photovoltaic system generates year-round energy necessary for the needs of the household