## 1 Introduction  
Increasing amounts of renewable energy sources (RESs), such as solar and wind, are required to be integrated into the electrical power grid, driven by the growing energy consumption demand as well as the concern for environmental protection. As a consequence, the power distribution network is shifting from a centralized energy supply system with a hierarchical framework to a multi-sourced, meshed network of the next generation--the Energy Internet [1, 2]. Emerging technologies in the areas of active distribution network [3, 4, 5], microgrid [6, 7, 8] and smart home energy management [9, 10, 11] are being developed rapidly to meet the requirements of the Energy Internet, in which energy production, distribution and consumption as well will all be scheduled and managed in a unified and optimal manner [1]. Flexible and bidirectional energy flow in the distribution network will be a symbolic feature of the Energy Internet, since microgrids and home area energy networks will play the dual roles of energy consumption and production. As the basic energy presumption unit, a home area energy network will be an essential part in the Energy Internet in terms of energy saving and management due to the following reasons: (1) residential and commercial buildings account for a considerable amount, e.g.,up to 50% in Europe, of the total energy consumption [12]; (2) enormous amounts of RESs, especially those small scale distributed generations (DGs) will be integrated into the power grid through home area energy networks; and (3) these numerous and widely allocated DGs will have critical influence on the distribution network due to the stochastic and intermittent nature of the RESs [13; 14]. Meanwhile, these DGs can also have comprehensive interactions with the grid, which can be used to improve power quality, energy efficiency and system stability of the distribution network. For energy prosumers at residential level with RES units of several kW capacity, an energy storage system (ESS), and perhaps electric vehicles (EVs) with feedback functionality, they can benefit the power grid in several aspects: (1) the total power generation and power transfer loss on the grid side can be reduced considerably; (2) with the introduction of flexible tariff on electric power, energy prosumers are encouraged to cooperate with the power grid to reduce the peak load [15]; (3) energy prosumers are capable of suppressing the impact of the RES output fluctuations and load consumption variations to the grid, by optimally scheduling the operations of their ESSs and EVs; and (4) meanwhile, a prosumer can cooperate with another neighbors in order to achieve better global performance and reliability within the residential microgrids [16]. Therefore, there is an urgent need to develop efficient and flexible methods for smart home energy management. Many works have been conducted in the field of smart home energy management, aiming to meet the requirements for DGs deployment in large scale. In paper [17], a home energy management scheduling controller of the residential demand response strategy was developed by using hybrid lightning search algorithm-based artificial neural network to predict the optimal on/off status for home appliances. Paper [18] investigated an optimal control scheme for a neighborhood of smart households with DGs in order to minimize total energy cost through energy transactions and dynamic pricing, while maintaining fair usage of the distribution transformer. Paper [19] developed a decision-support tool which was composed of an energy service model and a co-evolutionary version of particle swarm optimization-based DG scheduling algorithm, aiming to aid households in making more intelligent decisions when operating their major home appliances. Paper [20] proposed a ZigBee-based intelligent self-adjusting sensor, and presented a situation-based self-adjusting scheme and an event-based self-adjusting sensor network to reduce the home energy consumption.