Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications

Abstract

The artificial neural network reduces humanity and society’s burden to solve complex problems highly efficiently. Artificial neural networks resemble brain activities based on the acquired training samples used for various applications such as classification, regression, prediction, smart grid, natural language processing, image processing, medical diagnosis, and so on. This paper illustrates the different artificial neural network architectures, types, merits, demerits, and applications. Therefore, this paper provides valuable information to students and researchers to enrich their knowledge about an artificial neural network and research it. This paper also proposed a multilayer-perceptron-neural-network-based solar irradiance forecasting model, an improved backpropagation neural network-based rainfall forecasting model, and an Elman neural network-based temperature forecasting model. The performances of the proposed neural network-based forecasting models are analyzed with various hidden neurons and validated using the acquired real-time meteorological data. The proposed neural network forecasting models achieve rigorous results with reduced errors for the considered applications and aid sustainability.

Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods

“…ANN regression models are used in regression problems because they can solve complex non-linear relationships between input and output values by learning from a portion of the data and adapting accordingly [28]. The basic structure of an ANN model consists of an input layer, hidden layers, and an output layer [29].…”

Reviewing the Situation and Prospects for Developing Small Renewable Energy Systems in Poland

“…In particular, a dramatic shift in the use of digital technologies has been identified that will affect all aspects of work and life. How this change plays out remains largely dependent on our reactions to emerging trends and how they will be shaped [16,93]. The energy sector is one industry that also stands to gain significantly from the deployment of Artificial Intelligence [94,95] following the pandemic crisis.…”

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“…The architectures of ANNs are inspired by the workability of human neural networks in the performance of some tasks [17]. They are constructed as connected neurons, which form the following layers: input, output, and hidden.…”