The quality of data and the accuracy of energy generation forecast by artificial neural networks (original) (raw)

The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in southeastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data. 1. INTRODUCTION Electrical energy is one of the most commonly used sources of usable energy. Its generation was mainly based on natural resources, such as carbon, oil, gas, or radioactive elements. Diminishing resources of oil or gas, arduous effects for the natural environment caused by fossil-fuel power stations and waste from atomic power stations led to a greater interest in renewable energy sources. Recent years have seen very dynamic growth of both wind power station and solar power station. The generation of energy in both cases is highly changeable [1-3], as it directly depends on atmospheric conditions. The stability of energy system requires the balance between supply and demand of electrical energy, which in turn involves estimating the amount of energy that is to be produced by renewables at a given moment, therefore, the energy forecast from both sources is necessary. High accuracy of wind energy forecasts increases economic benefits by reducing energy generation costs and improves the security of energy system. The issue of predicting the energy generation by wind turbines is broadly discussed in the literature. The authors apply various forecasting methods, starting from the conservative method [4, 5], through statistical methods [5, 6], econometric methods [4], physical models, pseudo-intelligent methods, (artificial neural networks, fuzzy logic) [4, 7-9], and ending on hybrid methods [10-13]. The Presented forecasts concern the energy produced by individual turbines [4, 5, 10] as well as by whole wind burdened with smaller errormwhen compared to the ong-term forecasts which results in higher interest of them. The current paper, for forecasting purposes uses feedforward multilayer networks comparing its accuracy with the conservative (naïve) model. The presented in the paper have a short term.