Short-term load Forecast Based on Neuro-evolution Algorithm
Ahmed Tiguercha Ahmed Amine Ladjici Mohamed Boudour
industrial electrical power systems
Load forecasting is an important tool in power system planning, operation and control. Load forecasting ensures the equilibrium between consumption and production and, so, helps in maintaining system stability, and optimal operation of the electricity market. Neuroevolution leverage the strengths of two biologically inspired areas of machine learning: artiﬁcial neural net works and evolutionary algorithms. The basic idea of Neuro-evolution algorithm is to search the space of neural network policies directly using an evolutionary algorithm, and ﬁnd the best structure possible for the task at hand. Neuro-evolution can, therefore, improve the effectiveness of Neural Network by optimizing its structure in terms of complexity and efficiency using the optimization capabilities of evolutionary algorithms. The current paper presents a short-term load forecasting methodology, based on neuro-evolution algorithm. A comparative study is conducted between NE and two of the most used machine learning algorithms, artiﬁcial neural network (ANN), and Support Vector Regression (SVR).
This article is written in Adobe PDF format ( .pdf file ).To view this article you need to download the file. Please rightclick on the link below and then select "Save target as" to download the file to your harddrive.