Step by Step Solar Power forecasting using Neural Network

Step by Step Solar Power forecasting using Neural Network

MATLABSolutions demonstrate how to use the MATLAB software for simulation of This paper represents the Solar power forecasting is witnessing a growing attention from the research community. The paper presents an artificial neural network model to produce solar power forecasts. Sensitivity analysis of several input variables for best selection, and comparison of the model performance with multiple linear regression and persistence models are also shown.

Abstract

In recent years, the rapid boost of variable energy generations particularly from wind and solar energy resources in the power grid has led to these generations becoming a noteworthy source of uncertainty with load behavior still being the main source of variability. Generation and load balance is required in the economic scheduling of the generating units and in electricity market trades. Energy forecasting can be used to mitigate some of the challenges that arise from the uncertainty in the resource. Solar power forecasting is witnessing a growing attention from the research community. The paper presents an artificial neural network model to produce solar power forecasts. Sensitivity analysis of several input variables for best selection, and comparison of the model performance with multiple linear regression and persistence models are also shown.

Introduction

Variable energy generations, particularly from renewable energy resources such as wind and solar energy plants have created operational challenges for the electric power grid because of the uncertainty involved in their output in the short term. When the penetration level of the variable generation is high, the intermittency of these resources may adversely affect the operation of the electric grid. Thus, wherever the variable generation resources are used, it becomes highly desirable to maintain higher than normal operating reserves and efficient energy storage systems to manage the power balance in the system. The operating reserves that use fossil fuel generating units should be kept as low as possible to get the highest benefit from the deployment of the variable generations. Therefore, forecasting these renewable resources takes on a vital role in the operation of power systems and electricity markets.

The rest of the paper is organized as follows: Section II includes a review of statistical forecasting models for variable generations and a brief introduction to artificial neural networks (ANN). Section III describes the data used to build the ANN. Section IV discusses the various solar power forecasting modeling stages. Section V presents the results and evaluation of the models. Section VI provides the conclusions.

STATISTICAL VARIABLE GENERATION FORECASTING MODELS

Forecasting models are continuously being improved to generate more accurate forecasts of solar and wind power. In this section, the statistical models that use both non-learning and learning approaches are described.