## 1 Introduction  
Recently, the shortage of natural energy resources and growing energy demand in the world have resulted in dependency on renewable energy resources, such as solar and wind energy. According to [1], the power sector accounts for 38% of the expected energy demand increase by the year 2020. In Europe, the energy demand of the residential and building sector is expected to increase by 16%, the industry by 12% and the power sector by 13%. In order to fulfil extensive energy needs, electricity providers are thinking of re-organizing their energy production, transmission and distribution schedules. One of the current solutions is the transformation of the old grid into a smart grid (SG) with advanced information and communication technologies. As discussed in [2], SG has the ability to incorporate distributed, as well as renewable energy resources, which can mitigate the effects of a large number of electric vehicles, peak power plants, _etc._  
Demand response (DR) can be defined as the set of rules adopted by utilities to manage the end user energy demand in response to the electricity supply limits [3]. Different strategies have been used to motivate the end users to take part in DR programs to efficiently utilize energy consumption. The most widely-used DR programs include: critical peak pricing (CPP), time of use pricing (TOU), day ahead pricing (DAP), real-time pricing (RTP), flat rate pricing (FRP) and inclining block rate (IBR) [4; 5; 6]. The energy management controller (EMC) is used to take DR signals via a smart meter, which can use both price information and user inputs to generate schedules. Then, these schedules are transmitted to the smart appliances via Wi-Fi, ZigBee, infrared, _etc._[7; 8]. Generally, DR mechanisms work by either shifting the peak load to off-peak hours or reducing the overall energy consumption. The former encourages the users with incentive-based schemes to avoid high peaks to stabilize the grid. The latter adopts efficient energy consumption plans [9]. However, load shifting may be used for bill savings and eventually may disturb the end users comfort. Therefore, there is a trade-off between user comfort and bill savings. In order to achieve cost-effective energy usage with minimum user frustration, a mechanism is needed that is able to adopt and incorporate the advantages of centralized, decentralized and distributed energy management schemes. Population-based heuristic techniques for global optimization are widely used due to low complexity, fast convergence and processing time. Usually, users can assign their own energy consumption patterns and preferences, which do not require high computational power and capabilities. Moreover, users do not need to interact with other users of the utility directly, which saves time, communication bandwidth and security. Based on the aforementioned challenges, we use a heuristic-based wind-driven optimization (WDO) technique to select energy-efficient patterns for driving all appliances. The contributions of the paper are as follows. * We build a model by classifying electrical appliances into three groups based on power usage and user comfort requirements. This model incorporates the three proposed classes of appliances based on hourly electricity prices (TOU) during on-peak and off-peak hours in conjunction with user preferences. * On the proposed model, we devise a binary version of WDO algorithm for minimum electricity cost and maximum user comfort. Moreover, a knapsack-based WDO (K-WDO) algorithm is also designed for maximum electricity cost saving that can be used as a benchmark for the performance evaluation of energy consumption in home area networks. The min-max regret-based knapsack optimization technique is used to minimize the maximum energy consumption. * The said optimization techniques are mapped for scheduling electrical appliances.