Forecasting Europe nuclear electric power using artificial neural networks
Authors: Ashour Marwan , Hadi Zainab 
Published: April 2022

The recent upsurge in research activities into artificial neural networks (ANNs) has proven that neural networks have powerful pattern classification and prediction capabilities. ANNs have been successfully used for a variety of tasks in many fields of business, industry, and science. researchers and practitioners. Interest in neural networks is evident from the growth in the number of papers published in journals of diverse scientific disciplines. A search of several major databases can easily result in hundreds or even thousands of “neural networks” articles published in one year.One of the major application areas of ANNs is forecasting. There is an increasing interest in forecasting using ANNs in recent years. Forecasting has a long history and the importance of this old subject is reflected by the diversity of its applications in different disciplines ranging from business to engineering.The ability to accurately predict the future is fundamental to many decision processes in planning, scheduling, purchasing, strategy formulation, policymaking, and supply chain operations. As such, forecasting is an area where a lot of effort has been invested in the past. Yet, it is still an important and active field of human activity at present and will continue to be in the future.Forecasting has been dominated by linear methods for many decades. Linear methods are easy to develop and implement and they are also relatively simple to understand and interpret. However, linear models have serious limitations in that they are not able to capture any nonlinear relationships in the data. The approximation of linear models to complicated nonlinear relationships is not always satisfactory. In the early 1980s, Makridakis (1982) organized a large-scale forecasting competition (often called M-competition) where a majority of commonly used linear methods were tested with more than 1,000 real-time series. The mixed results show that no single linear model is globally the best, which may be interpreted as the failure of linear modeling in accounting for a varying degree of nonlinearity that is common in real-world problems.ANNs provide a promising alternative tool for forecasters. The inherently nonlinear structure of neural networks is particularly useful for capturing the complex underlying relationship in many real-world problems. Neural networks are perhaps more versatile methods for forecasting applications in that not only can they find nonlinear structures in a problem, they can also model linear processes. For example, the capability of neural networks in modeling linear time series has been studied and confirmed by several researchers.Research efforts on neural networks as forecasting models are considerable and applications of ANNs for forecasting have been reported in a large number of studies. Although some theoretical and empirical issues remain unsolved, the field of neural network forecasting has surely made significant progress during the last decade. It will not be surprising to see even greater advancement and success in the next decade.The goal of this paper is to predict electrical energy consumption using nonlinear autoregressive (NAR) models. The practical section contains historical data on Europe's nuclear electric power by year from 1980 to 2006. It is recommended that further research be undertaken in the following areas Intelligent forecasting methods are being used as an alternative to traditional forecasting methods.