Abstract
Solar thermal systems (STS) are efficient and environmentally safe devices to meet the rapid increasing energy demand now a days. But it is very important to optimize their performance under required operating condition for efficient usage. Hence intelligent system-based techniques like artificial neural network (ANN) play an important role for system performance prediction in accurate and speedy way. In present paper, it is attempted to scrutinize the approach of ANN as an intelligent system-based method to accurately optimize the performance prediction of different solar thermal systems. Here, 25 research works related to various solar thermal systems have been reviewed and summarized to understand the impact of different ANN models and learning algorithms on performance prediction of STS. Using ANN, a brief stepwise summary of researchers’ work on various STS like solar air heaters, solar stills, solar cookers, solar dryers and solar hybrid systems, their predictions (results) and architectures (network and learning algorithms) in the literature till now, are also discussed here. This paper will genuinely help future researchers overview the work concisely related to solar thermal system performance prediction using various types of ANN models and learning algorithm and compare it with other global methods of machine learning. Citation: Ahmad, A., Ghritlahre, H. K., and Chandrakar, P. (2020). Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review. Trends in Renewable Energy, 6, 12-36. DOI: 10.17737/tre.2020.6.1.00110
Highlights
Energy is a primary feed in for almost all activities and economic development
Since energy is imperative to execute the operation of production, transport, agriculture and household services, the process of economic growth requires higher proportion of energy consumption, which forces us to focus on ensuring its continuous supply to meet our ever-rising demands [1-4]
Besides conventional energy sources like coal, petroleum and natural gas, some non-conventional energy sources known as renewable energy sources are solar energy, wind energy, tidal energy and bioenergy
Summary
Energy is a primary feed in for almost all activities and economic development. there is an ultimate dependency between the energy availability and the growth of a nation. ANN is a powerful data-driven, self-adaptive, flexible computational tool having capability of handling large amount of data sets This technique is found very suitable for implicitly detecting complex non-linear relationship between dependent and independent variables with high accuracy. The neuron collects multiple inputs in combination with attachment weights from other neurons and performs a nonlinear activation process and generates a single output data that can go to the other neurons Such input data is analyzed by the neurons and transferred to the network layer. In the RBF model, the signals are collected at the input layer and passed through the hidden layer of the second layer, which generates the output data [48, 49]. Generalized regression neural network (GRNN) technique is a probabilistic model between an independent (Input) and dependent (Output) variables. This results in the predicted value y to input vector x as below [48]:
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