Optimal Price Based Demand Response of HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants Buildings

Retail Electricity Pricing Strategy via an Artificial Neural Network-Based Demand Response Model of an Energy Storage System

“…The given electricity prices and timedelayed DR schedules are used as the input data of the ANN model, and the current DR schedule is adopted as the output. With that dataset, an unknown black-box model can be trained by a machine learning algorithm to become a white-box model capable of describing the relationship between the inputs and outputs [27]- [29]. Likewise, the obtained dataset is used to train and test the proposed ANNbased DR model, which will be discussed in Section III-B, to determine the relationship between the electricity prices and optimal DR profiles using the ANN architecture shown in Fig.…”

A Federated Learning System for Optimised Environmental Control of Consecutive Areas

“…Finally, the work in (Kim, 2018) focuses on a similar challenge HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants. The work shows how occupants thermal preference, a neural network modelling algorithm, a grid tie strategy and variable speed heat pumps are used to enhance productiveness and gain a reduction in energy consumption, similar to this approach the research outlined plans to show the practical application of how monitoring information, a neural network, historical data and a reduced element of user input metrics might be used in a neural network to reduce power network drain and accurately pinpoint spikes in demand throughout the day at a given area hence reducing the cost to maintain existing infrastructure and reform transmission loses proportionately.…”

“…A relay was configured to actuate the heating and cooling system states. The intention is to recreate in scale for issues experienced as in (Gopika, 2015), (Vatanparvar, Burago, & Abdullah, 2018), (Kim, 2018) and (Ardiyanto, et al, 2018). Figure 3 illustrates the configuration of the scaled model.…”

A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response

“…An HVAC system is considered in [6] that is controlled (in combination with other type of loads, PV generation and storage) by a home energy management system, thus enabling residential consumers to participate in demand response programs. A price-based demand response strategy for multi-zone office buildings to optimize the energy cost of HVAC units and the thermal discomfort of occupants is formulated in [7] as a MILP model. The authors in [8] develop an approach based on a partial-differential equation model of thermal diffusion to determine the thermostat settings to minimize the electricity bill for a consumer with energy time-of-use and power prices, in which the optimal thermostat programming for HVAC is formulated as a constrained dynamic optimization problem.…”