Abstract
Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.
Highlights
Equipment maintenance plays a key role in resuming and keeping regular operational capabilities of military equipment [1]
We propose an approach of forecasting electrical energy consumption of equipment maintenance using artificial neural network (ANN) and particle swarm optimization (PSO)
Implement three other approaches: the ANN trained by BP, the ANN trained by basic PSO, and the nonlinear regression model optimized by PSO [41], in order to comparatively evaluate the performance of our method
Summary
Equipment maintenance plays a key role in resuming and keeping regular operational capabilities of military equipment [1]. There are a number of methods and models for forecasting electric energy consumption [5] Conventional forecasting approaches, such as principal component analysis, least error square (LES), and regression techniques [5,6,7,8], become difficult and impractical to provide an adequate forecasting of electrical energy consumption, mainly due to the ever increasing complexity and diversity of equipment maintenance demands. Those approaches have some inherent limitation and disadvantages such as requirement for large training datasets, sensitivity to noise, and low capability of handling missing data. The rest of the review paper is synthesized as follows: Section 2 reviews the related work of the application of ANNs in electrical energy consumption forecasting and the related problems, Section 3 presents our approach in detail, Section 4 presents the computational experiments and the real-world applications, and Section 5 concludes
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