Forecasting of Energy Power with Hybrid Multi-Variant Deep Belief Network
Keywords:
Deep Belief Network, Convolutional Neural Network, Mean Absolute Error, ReLu Activation Function, Mean Square ErrorAbstract
Prediction of renewable energy power plays a vital role in the development of national economics. Because of the non-linear behavior of the climatic and environmental factors, predicting the energy power becomes quite challenging for the researchers. In recent years, the evolution of Deep Belief Networks has become popular in various domains since it handles the non-linear features for time series data and yields a promising result. In this article, three different models namely DBN, multi-variant CNN, and hybrid multi-variant CNN-DBN model were proposed and their performance metrics such as MSE, RMSE, MAP, and MAPE were evaluated and discussed in detail. It is evident from the value of the metric that the hybrid multi-variant CNN-DBN outperforms the other two models DBN and Multi-variant CNN.
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