Stochastic home energy management system via approximate dynamic programming

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

“…The algorithmic comparison is beyond the scope of this paper and will be conducted in future work. For a much simpler HEMS problem (without considering occupant behaviors), interested readers can find more details about the algorithmic comparison in the prior work [34].…”

“…Note that the Sobol sampling backward induction (SSBI) and K-D tree nearest neighbor (KDNN) techniques [34] are adopted here to increase the computational efficiency of the ADP-HEMS. Analogous to classic backward induction, SSBI contains a Sobol sampling function that compresses the workload efficiently by only considering necessary state, action, and uncertainty sets.…”

“…KDNN is a noteworthy value function approximation approach that traverses the nearest point in the state space and seeks an approximation value of O * t instead of computing the exact value from Equation (1). The combination of KDNN and SSBI has demonstrated significant increases in the computation speed of the ADP in finding the optimal policy a * [34].…”

“…To tackle those problems, this paper studies the HEMS, consisting of HVAC, EV, and PV, by taking into account three distinct categories of occupant behaviors: (1) clothing behaviors, (2) EV SOC concerns, and (3) PMV-based thermal comforts. A stochastic Adaptive Dynamic programming (ADP) model [34] is modified and extended to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant's clothing decisions and the EV's charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV's arrival SOC. Nonlinear and nonconvex models of thermal discomfort, EV SOC concerns and clothing behaviors are holistically embedded in the ADP-HEMS model.…”

Aggregating buildings as a virtual power plant: Architectural design, supporting technologies, and case studies

“…Rather than being end energy takers, buildings with renewable energy source installations are now capable of providing largecapacity energy provision to support the grid-once they are aggregated to scale and managed properly. The increasing prevalence of Building Energy Management Systems (BEMSs, or known as Home Energy Management Systems [HEMSs] when the concept is applied to residential buildings) [12][13][14] further drive buildings to become autonomous and intelligent energy entities. As a kind of expert system, a BEMS acts as a building's 'brain'; it centrally and intelligently manages the building's energy resources to optimise the building's operational energy performance subjected to different objectives.…”