A Demand Response Implementation with Building Energy Management System

Abstract

The demand response (DR) program is one of the most promising components in the development of the Smart Grid. However, there are many challenges in practical operation to improve the existing and outdated system to comply with the DR programs. In Thailand, the major pain point of the office building owner in the DR program is the additional equipment, modification and operation cost of the existing equipment. Moreover, the sophisticated solution and control are other obstacles that need more measurements and data, and they make the operation difficult to work with. In this paper, we implemented a simple yet cost-effective hardware and software solution targeting an outdated air-conditioning system without voiding the warranty of the outdated equipment and without installing any additional measurements. In addition, the proposed operation is designed to be easy to operate under the equipment limitation and unskilled labor. More importantly, indoor temperature setpoint schedules during the DR event are forecasted with some public datasets to determine the capacity of the energy management system that can reduce the power consumption in the office building without an effect on the occupants’ comfort. To confirm the practicality of the proposed solution, the actual operation of the proposed solution can achieve the maximum power reduction at 19.80 kW (43.79% of the maximum power consumption) while keeping only 1 °C of difference from the typical room temperature (26–28 °C).

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

“…A combination of the weighted moving average (WMA) and linear approximation is used by Varzaneh et al [41] to predict the next day's Various prediction algorithms exist in the residential EMS literature for machine learning-based forecasting methods. Charoen et al [44] predict the temperature setpoint of an air-conditioning system by applying an artificial neural network (ANN) with three fully connected layers while considering the features: outdoor temperature, outdoor humidity, and weather conditions. In contrast to most ANNs, which are primarily trained offline with historical datasets to obtain fixed weights and biases, Youssef et al [56] Further, they test four typical activation functions: sigmoid, hyperbolic tangent, rectified linear unit, and exponential linear unit.…”

“…LoRaWAN is especially suited for buildings due to the penetrability of walls compared with others, such as Wi-Fi, Bluetooth, ZigBee, or Z-Wave. While Charoen et al [44] connect their devices via Wi-Fi, Tantawy et al [38] link all smart home appliances, RESs, and ESS over a home area network, considering ZigBee, Z-Wave, and Wi-Fi as communication technologies.…”

Stakeholder-driven carbon neutral pathways for Thailand and Bangkok: integrated assessment modeling to inform multilevel climate governance

“…Power system digitalization and modernization will be crucial to support high shares of intermittent renewable energy sources in Thailand's power grid, and to minimize costs associated with backup capacity and storage. Examples include load forecasting and monitoring, demand response to manage peak load, building energy management systems, and smart EV charging stations (Junlakarn et al, 2017;Charoen et al, 2022;Chanraksa and Singh, 2023). These measures could be particularly effective in the Bangkok metropolitan area as it is a major electricity demand center.…”

Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption

“…Many research surveys [23][24][25] have sought to understand these consumer preferences about energy consumption and their perceptions related to demand response and energy efficiency behaviors. Several authors [7][8][9]26] state that individual behaviors of office buildings users are the most relevant aspect that influences energy consumption.…”