Dynamic Neural Network Model Design for Solar Radiation Forecast

Abstract

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.

Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions

“…The hidden layer depicts the data aggregation and activation process, and the output layer represents the hidden layer output aggregated into the model output. This W-DNN architecture was developed in our previous research [17].…”

“…Applying equation (17) to the testing data (out sample) increases the model's performance for forecasting the intensity of solar radiation; initially, the RMSE was 17,680 to 16.3169. Graphically, the graph quality of testing data (out sample) is shown in Figure 4.…”

“…The implementation of a DNN model using a recurrent neural network (Elman and Jordan) has been carried out by [11] to predict weather patterns, especially temperature, rainfall, and solar [17] using meteorological data in the form of wind speed, wind direction, air humidity, air pressure, and rainfall to model and predict the intensity of solar radiation in Mataram City, West Nusa Tenggara using the DNN model to produce significant forecasts.…”

“…In particular, research in [9] showed that the Wavelet Neural Network Model (static) could effectively model and estimate solar intensity. Furthermore, [17] has successfully modeled and predicted solar radiation intensity using the DNN model.…”

Fuzzy Wavelet Dynamic Neural Network Model for Modeling the Number of Tourist Visits to West Nusatenggara Province

“…Soft computing techniques are based on several methods, including fuzzy logic, neural networks, genetic algorithms, wavelets, and their combinations. These methods, either individually or in combination, have been used successfully to address time series problems [3][4][5][6][7]. Among the advantages of soft computing methods, whether used partially or in hybrid approaches, are their adaptive capabilities, self-learning algorithms, generalization abilities, the ability to solve complex and intricate nonlinear problems, and their capacity to handle uncertainty.…”