The cost of power engineering is a multi-variable and non-linear complex process. In the past, the cost of power engineering projects often requires analysis and research personnel to estimate the project cost through empirical analysis and subjective estimation. How to use the historical cost data of existing projects to build cost management for new power projects. Providing more reasonable judgments and comparative scientific theoretical analysis requires the use of accurate data mining knowledge. Based on the characteristics of “the interaction of many cost elements in power engineering and ultimately in engineering cost”, this paper applies BP neural network to the research of power engineering cost, and builds a rapid analysis model of engineering cost. The simulation study determined the feasibility and effectiveness of the model.

Xiao Xue, Nan Zhe. Research on Power Engineering Cost Forecasting Model Based on Artificial Neural Network. Academic Journal of Business & Management (2019) Vol. 1, Issue 3: 10-16. https://doi.org/10.25236/AJBM.2019.010302.

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