A Relay-Assisted (RA) network with relay selection is considered as a type of effective technology to improve the spectrum and energy efficiency of a cellular network. However, loading balance of the assisted relay node becomes an inevitable bottleneck in RA network development because users do not follow uniform distribution. Furthermore, the time-varying channel condition of wireless communication is also a major challenge for the RA network with relay selection. To solve these problems and improve the practicability of the RA network, a Loading Balance-Relay Selective (LBRS) strategy is proposed for the RA network in this paper. The proposed LBRS strategy formulates the relay selection of the RA network under imperfect channel state information assumption as a Multistage Decision (MD) problem. An optimal algorithm is also investigated to solve the proposed MD problem based on stochastic dynamic program. Numerical results show that the performance of the LBRS strategy is better than that of traditional greedy algorithm and the former is effective as an exhaustive search-based method.

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Given the energy wastage problem of the laminar cooling system of hot rolling, the dynamic optimization scheduling problem of the system under complex intermittent working conditions was investigated to improve the effective utilization of resources. First, combined with production schedules and rolling rhythm times, an On-line Sequential Extreme Learning Machine (OS-ELM) based laminar cooling water consumption prediction model was developed for predicting water demand trends to maintain system supply-demand balance at pump stations. Then, the scheduling instruction optimization model, which considers the system intermittent production conditions and the feedback optimization mechanism, was proposed to reduce the system cooling water overflow and smooth the high cistern level fluctuations. And an adaptive operation scheme optimization model of pumping station based on scheduling instructions was proposed to minimize the total shaft power of pumping station. Finally, the strategies were solved using an improved sparrow search algorithm (ISSA). Experimental simulations demonstrate the effectiveness and supremacy of the proposed method for industrial energy saving in complex intermittent conditions.

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