A Survey on Home Energy Management <s> 5) APPLIANCE SCHEDULING <s> We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from $16\%$ to $40\%$ with respect to a price-unaware energy allocation. <s> BIB001 </s> A Survey on Home Energy Management <s> 5) APPLIANCE SCHEDULING <s> In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation. <s> BIB002
Artificial neural networks (ANNs), as a class of universal approximators, can learn to solve scheduling problems by means of supervised training. Among ANN topologies, feedforward architectures are commonly chosen, while considering as inputs, for instance, future demands and generation forecasts, time of day and occupancy information. When multiple devices need to be simultaneously managed, two strategies can be followed. One considers training an individual ANN for each appliance BIB002 , while the other approach considers training a single ANN to control multiple devices  . Reinforcement learning (RL) has evolved around the concept of an intelligent agent in a dynamic environment. This agent iteratively learns how to best act while performing a given task. At each iteration, the agent observes and evaluates the current environment state, takes an action from a previously defined set and receives a reward as a result of the conducted action. The goal of an agent is to either maximise rewards or their expected values BIB001 .