Fei Xiong

Zhenyu Qiao, Bo Yang, Qimin Xu et al.

High penetration of renewable energy source makes microgrid (MGs) be environment friendly. However, the stochastic input from renewable energy resource brings difficulty in balancing the energy supply and demand. Purchasing extra energy from macrogrid to deal with energy shortage will increase MG energy cost. To mitigate intermittent nature of renewable energy, energy trading and energy storage which can exploit diversity of renewable energy generation across space and time are efficient and cost-effective methods. But current energy storage control action will impact the future control action which brings challenge to energy management. In addition, due to MG participating energy trading as prosumer, it calls for an efficient trading mechanism. Therefore, this paper focuses on the problem of MG energy management and trading. Energy trading problem is formulated as a stochastic optimization one with both individual profit and social welfare maximization. Firstly a Lyapunov optimization based algorithm is developed to solve the stochastic problem. Secondly the double-auction based mechanism is provided to attract MG truthful bidding for buying and selling energy. Through theoretical analysis, we demonstrate that individual MG can achieve a time average energy cost close to offline optimum with tradeoff between storage capacity and energy trading cost. Meanwhile the social welfare is also asymptotically maximized under double auction. Simulation results based on real world data show the effectiveness of our algorithm.

Yu Li, Fei Xiong, Ziyi Wang et al.

Nowadays, artificial neural networks are widely used for users' online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users' intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users' intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users' actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.