Consumption Optimization in an Office Building Considering Flexible Loads and User Comfort This paper presents a multiperiod optimization algorithm that is implemented in a Supervisory Control and Data Acquisition system. The algorithm controls lights and air conditioners as flexible loads to reduce the consumption and controls a dishwasher as a deferrable load to implement the load shifting. Several parameters are considered to implement the algorithm for several successive periods in a real building operation. In the proposed methodology, optimization is done regarding user comfort, which is modeled in the objective function related to the indoor temperature in each room, and in the constraints in order to prevent excessive power reduction, according to usersxe2x80x99 preferences. Additionally, the operation cycle of a dishwasher is included, and the algorithm selects the best starting point based on the appliance weights and power availability in each period. With the proposed methodology, the building energy manager can specify the moments when the optimization is run with consideration of the operational constraints. Accordingly, the main contribution of the paper is to provide and integrate a methodology to minimize the difference between the actual and the desired temperature in each room, as a measure of comfort, respecting constraints that can be easily bounded by building users and manager. The case study considers the real consumption data of an office building which contains 20 lights, 10 ACs, and one dishwasher. Three scenarios have been designed to focus on different functionalities. The outcomes of the paper include proof of the performance of the optimization algorithm and how such a system can effectively minimize electricity consumption by implementing demand response programs and using them in smart grid contexts.

Smart RoombyRoom HVAC Scheduling for Residential Savings and Comfort HVAC systems account for a significant amount of the energy spent in residences with forcedair systems. Moreover, HVAC is a major contributor to greenhouse gas and fine particulate matter emissions. Recently, the use of sensors, wireless, microprocessors, and the Internet of Things (IoT) have been proposed to increase energy efficiency in residential buildings by controlling the duty cycle of furnaces and airconditioners. In this paper, we propose ILPSS (Integer Linear Programming for Smart Scheduling), a novel solution for optimizing the duty cycle of the HVAC equipment and improving usersxe2x80x99 comfort by allowing users to specify comfort levels at specific times in each room of a residence. Our proposal builds on multiplevariable, linear regression model and integer linear programming and can be run on a home IoT hub. Our experimental evaluation on a real dataset shows that our proposed solution saves energy (up to 45%) and meets usersxe2x80x99 comfort needs, compared to commodity and current smart HVAC systems.