A hybrid approach based on inverse neural network to determine optimal level of energy consumption in electrical power generation. (August 2019)

• Record Type: Journal Article
• Authors: Jahangoshai Rezaee, Mustafa; Dadkhah, Mojtaba
• Is Part Of: Computers & industrial engineering. Volume 134(2019)
• Page Start: 52
• Page End: 63
• Publication Date: 2019-08
• Subjects: Inverse neural network -- Nonlinear ill-posed problem -- Nonlinear mathematical programming -- Data envelopment analysis -- Electric power generation -- Optimization of energy resources consumption; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.05.024
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: Proposing a hybrid approach for determining optimal level of energy consumption. Integrating DEA, neural network and inverse neural network. Proposing an algorithm for reducing solutions' space in nonlinear programming model. A real case study for implementing proposed approach in electrical power generation of the thermal power plant. Abstract: Power plants are a strategic infrastructure industry in each country and provide a strong driving force for the development of other industries. Hence, the performance of a power plant should be optimized by adjusting input parameters to maximize efficiency. This paper mainly proposes a novel hybrid approach that aims to present an optimal level of input parameters of a power plant integrating Data Envelopment Analysis (DEA), Artificial Neural Network and Inverse Problem in a new way. The inputs for a power plant system include internal electricity, fossil fuel and water consumption which are used to generate electrical power. First, the collected data are evaluated using a DEA model to identify and determine the days that operated efficiently. Then, the efficient vectors including resources consumption and generated power in the days evaluated in the previous stage are entered into the artificial neural network. Afterward, the weights and biases of the trained network are extracted for use in the inverse neural network. The inverse neural network model is presented as a nonlinear optimization problem with Gaussian constraints. Because of the severely ill-posed nature of the problem, to solve and obtain reasonable solutions, an innovative approach is used to determine the optimal level of inputs in less time and more accuracy. Based on this approach, for solving the nonlinear model and reducing the number of candidate solutions, the optimal interval is computed for each input and each day. A real case study of the thermal power plant in Iran is presented to show the abilities of the proposed approach.