A Human-Centered Power Conservation Framework Based on Reverse Auction Theory and Machine Learning

Energy savings and occupant thermal comfort are the two most important factors in controlling heating ventilation and air conditioning (HVAC) operation in buildings. Typically, it is found that thermal comfort is not always met in buildings. Hence, there is still an opportunity to improve indoor thermal comfort, and at the same time save energy by controlling HVAC set points. The objective of this paper is to propose a method to obtain energy savings by adjusting HVAC set points based on occupant comfort measured using Predicted Mean Vote (PMV) and occupancy information. The idea is to calculate hourly PMV values based on real-time occupancy information, indoor temperature set points and humidity in a building. Then, a new set of temperature set points that can maintain occupant comfort, i.e., PMV = 0, is derived. To evaluate the effectiveness of the proposed method, a building model is developed in eQUEST using the information from a real-world building located in Alexandria, VA. Research findings indicate that HVAC electrical consumption savings of 14.58% is achieved when the proposed set point adjustment method is implemented as compared to that of the base case. To study the impact of adding occupancy information on HVAC energy savings, another scenario is simulated where HVAC set point is increased when the building is unoccupied, e.g., during lunchtime or holidays. Research findings indicate that additional HVAC electrical consumption savings of 8.79% is achieved when taking into account occupancy information in HVAC control.

The Smart Grid, a fusion of digital technologies and advanced communication methods, enables the transformation of power distribution, transmission, and generation by responding to fluctuations in electricity consumption. Conventional electrical grids often leave consumers unaware of their energy usage patterns, resulting in both energy wastage and financial loss. To enhance energy efficiency, the consumption patterns of consumers need regulation. Smart Grids efficiently employs Demand-Side Management (DSM) strategies, including peak clipping, load shifting, and consumer awareness campaigns, to optimize energy consumption and achieve energy savings. DSM’s synergy with smart meters and smart energy management systems (SEMS) emerges as a powerful trio in comprehensive energy conservation and optimization. The integration of SEMS with the Internet of Things (IoT), augmented by the advancements in 5G technology, emerges as a transformative paradigm. SEMS, operating within the IoT ecosystem bolstered by 5G connectivity, facilitates the instantaneous and efficient integration of IoT in SEMS, enabling real-time data collection, in-depth analysis, and data-driven decisions for optimal energy management. This empowers users with the ability to make data-driven decisions, yielding tangible outcomes in the form of efficacy, cost reductions, and fortified sustainability initiatives. We present a SEMS that amalgamates three microcontroller units to create a smart meter that is further integrated with a cloud-based middleware module and consumer API. Furthermore, it collects vital power metrics such as voltage, current, kWh, kW, and PF. Consumers can conveniently access this data in real-time via an API. Energy service companies can leverage this data for incentive programs and to motivate customers to optimize their energy consumption. These datasets serve as a foundational element for the development of diverse DSM strategies. This research work presents the deployment, and performance evaluation of an IoT-based SEMS that is implemented in diverse settings, including industrial, commercial, building, and warehouse setups. Moreover, as a test case, our evaluation extends to the specific conservation of energy within air-conditioning systems. The SEMS achieves significant energy conservation, with calculated savings ranging from 5% to 53%, showcasing its effectiveness in targeted energy management. The real-time data and case studies further demonstrate the efficiency of the presented work.

Optimization of electrical energy waste in house using smart appliances management System-A case study

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

Application of optimization techniques on lumped HVAC models for energy conservation

A robust heating, ventilation, and air conditioning (HVAC) system is needed to maintain a healthy and comfortable indoor environment. However, HVAC systems are responsible for significant energy usage in the United States, and enhancing current systems and implementing additional HVAC sensing are primary strategies for reducing energy consumption. This research developed an HVAC control algorithm (CA) that optimized ventilation operations within a conference room in the University of Virginia Link Lab. Using indoor air quality (IAQ), occupancy, weather, and HVAC operation data streams, the CA recommended a decision to ventilate or not ventilate the conference room every 15 minutes by comparing the cost of lost occupant productivity due to poor IAQ to the energy cost of ventilating the space. The ventilation decision with lower total cost was recommended. This project addressed scheduling inefficiencies of the current HVAC control system, which operates at full power throughout the day regardless of occupancy status. The CA reduced ventilation during unoccupied periods. The CA was tested over two months of historical data from October to December 2021 and recommended ventilating the conference room 15.13 percent of the time. During the same period, the standard system ventilated the conference room 49 percent of the time. Energy savings due to decreased operation were considerable and averaged 424 dollars per month, although these energy savings came at the cost of lost occupant productivity totaling 522 dollars per month. Future work on lost occupant performance will more accurately model the effects of reduced ventilation. However, annual energy savings of 5,000 dollars from a single conference room is encouraging, and scaling a similar CA to consider a set of rooms or an entire floor of a building could result in substantial energy conservation.

Net-zero Nation: HVAC and PV Systems for Residential Net-Zero Energy Buildings across the United States

Renewable energy in cities: by the Center for Renewable Resources. Van Nostrand Reinhold Co., New York, 1984

Development of a temperature control model used in HVAC systems in school spaces in Mediterranean climate

A review of different strategies for HVAC energy saving

Research on phase change materials (PCM) as a potential technology to reduce peak loads and heating, ventilation and air conditioning (HVAC) energy use in buildings has been conducted for several decades, resulting in a great deal of literature on PCM properties, temperature, and peak reduction potential. However, there are few building energy simulation programs that include PCM modeling features, and very few of these have been validated. Additionally, there is no previous research that indicates the level of accuracy when modeling PCMs from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation. The impact of accurately modeling realistic, nonlinear enthalpy profiles for PCMs versus simpler profiles is analyzed based on peak load reduction and energy savings using the conduction finite difference (CondFD) algorithm in EnergyPlus. The PCM and CondFD models used in this study have been previously validated after intensive verification and validation done at the National Renewable Energy Laboratory. Overall, the results of this study show annual energy savings are not very sensitive to the linearization of enthalpy curve. However, hourly analysis shows that if simpler linear profiles are used, users should try to specify a melting range covering roughly 80% of the latent heat; otherwise, hourly results can differ by up to 20%.

The main objective of this on-site study is to use a full-scale HVAC (Heating, Ventilating and Air-Conditioning) system installed in an office building in Taiwan for comparing the power consumption, energy-saving, and CO2reduction by variable primary flow (VPF) chilled water systems for controlling the HVAC. The on-site experimental results indicate that average power consumptions are 44,935 kWh for the constant volume (CV) chilled water systems, and 39,025 kWh for the VPF system. Approximately 19% of the maximum energy-saving rate can be achieved if the HVAC system is operated as a VPF system. Additionally, the reduced quantity of accumulated CO2emission varies from 0 to 44,186 kg with 0.623 kg-CO2kwh-1emission factor during the office hours of 08:00 (a.m.) to 20:00 (p.m.). The results demonstrate that switching the operation of an office building HVAC system from CV to VPF will significantly enhance energy savings and CO2reduction. This study is will offer useful information for evaluating an indoor environmental policy with respect to energy savings and CO2emission reduction for office HVACs used in subtropical regions.

Optimization of thermal processes in a variable air volume HVAC system

The increasing world population has led to a significant increase of energy consumption in the building sector in recent years. Reasons are manifold. They mainly include increased thermal comfort requirements for the building occupants. Higher thermal comfort implies higher needs for the buildings' heating, ventilation and air-conditioning (HVAC) systems at their peak levels. This operation eventually entails the risk of load peaks, which increases risks for electric black-outs. Demand side management (DSM) is an established concept for buildings, which includes the optimal shifting of potential loads to time periods such that peak loads can be prevented effectively. The concept thus provides that electric and thermal storage potentials are exploited adequately. Further development should enforce the use of renewable energy sources, which offers another possibility to efficiently reduce the risks of potential load peaks. Mathematical modeling of HVAC systems is an established approach to understand the system behavior and to trigger plans and strategies on how to tackle the HVAC system controls in order to recognize potentials and consequently perform efficient load shifting in an automated way. Model based control concepts endorse the systematic and transparent approach towards automated load shifting. This paper presents the methodology for the design of a model based predictive control concept for load shifting in the non-residential building sector. The methodology includes the mathematical modeling of the HVAC systems and the loads of a passive house standard non-residential building. The concept for the model based predictive controller is developed upon a simplification of the plant model.

Load shifting and energy conservation using smart thermostats in contemporary high-rise residential buildings: Estimation of runtime changes using field data