Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Alma Y Alanis,Jesus G Alvarez,Oscar D Sanchez

doi:10.3390/math9101075

Abstract

Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

Highlights

Nowadays, the demand for electricity is growing rapidly as a result of social, economic and industrial development, while the reserves of fossil fuels for power generation are rapidly reducing and pollution is increasing
This paper presents an approach that is capable of improving forecasting ability using fewer input parameters and simulation time; that is, this paper focuses on the development of a simplified and efficient forecasting method for time series in wind energy systems, which is very important for future wind energy system planning and crucial for control, scheduling, maintenance and resource planning of wind energy conversion systems
We propose on-line training to continually adjust the parameters of a recurrent high order neural network (RHONN), which is used to forecast multiple steps forward by the recursive strategy

Summary

Introduction

The demand for electricity is growing rapidly as a result of social, economic and industrial development, while the reserves of fossil fuels for power generation are rapidly reducing and pollution is increasing. Due to the continuous increase of wind energy implementation in power systems, the problems caused by the volatile nature of wind speed and the occurrences in system operations, such as scheduling and dispatch, have drawn the attention of system operators, public services and researchers, for the development of state-of-the-art power, wind speed and price forecasting methods. These methods have the necessary ability to reduce the influence of intermittent wind energy on system operations, as well as the capability to harvest wind energy efficiently [5]

Methods

Results

Discussion

Conclusion