Evaluation of the Use of Artificial Neural Networks to Predict the Photovoltaic Power Generation Factors by Using Feed Forward Back Propagation (FFBP) Technique

Electrical And Electronics Engineering, National Institute Of Technology, Puducherry, India ,Shreeniket Trivedi

doi:10.47191/ijcsrr/v4-i2-06

Electrical And Electronics Engineering, National Institute Of Technology, Puducherry, India , Shreeniket Trivedi

Open Access

https://doi.org/10.47191/ijcsrr/v4-i2-06

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Abstract

The solar power generation (renewable energy) is the cleanest form of energy generation method and the solar power plant has a very long life and also is maintenance-free, but due to the high unpredictability of the generated solar power due to dynamically changing environmental factors it cannot be used as the reliable source of power. This prevents the maximum utilization of solar energy. In this project we are designing the artificial neural network model to predict the power generated depending on the various environmental factors like visibility, cloud cover (sky cover), etc. the intensity of the incident of the solar radiation decreases and thus the plant is not able to work at its rated capacity. We use Artificial Neural Network (ANN) with Feed Forward Back Propagation (FFBP) technique and predicted the percentage of the maximum plant capacity which will be generated by considering the environmental factors like temperature, pressure, distance to solar noon, day light, sky cover, visibility, humidity, wind speed, wind direction and compared our results with available data and find quite encouraging results.

Highlights

The photovoltaic energy generation is the cheapest and cleanest form of energy generation method
Two broad section is studied that can help best predict solar power, firstly, the study of the environmental factor that affect the production of solar power by solar cell and the study of the working of Artificial Neural Network (ANN) to predict photovoltaic energy generation
TECHNIQUEUSED TO DESIGN THE MODEL In our working model design we have taken environmental data as inputs and used Feed Forward Back Propagation (FFBP) technique for training the ANN model to predict the probability of generated power out of total capacity of solar power plant

Summary

INTRODUCTION

The photovoltaic energy generation is the cheapest and cleanest form of energy generation method. To match the load and the generation of the central power grid we must connect the power generation plant to the central grid in the most economically efficient and reliable manner. An accurate prediction of power generation in the solar energy plant is essentially required. It is important to determine parameters that can help in best possible prediction in regard of photovoltaic power generated [3]. Two broad section is studied that can help best predict solar power, firstly, the study of the environmental factor that affect the production of solar power by solar cell and the study of the working of Artificial Neural Network (ANN) to predict photovoltaic energy generation. Our project works on the goal to make solar power plant a reliable source of power

TECHNIQUEUSED TO DESIGN THE MODEL

COMPUTATION USING MATLAB CODE Data Preparation

Discussion