- Human-Building Interaction for Energy Conservation in Office Building
The study aims to increase energy awareness through a reminder system which delivers simple and complex messages to occupants in order to change their energy consumption behaviour. While simple messages only involve suggestions about how occupants can behave to decrease energy consumption, complex messages additionally include information about the consequences of the suggested behaviours. The point emphasized in the study is that intensive feedbacks that contain the outcomes of the occupants' behaviour are more powerful than simple suggestions to convince people to adopt green habits.
- Towards Robust Multi-Objective Optimization Under Model Uncertainty for Energy Conservation
The aim of the work is to maximise energy savings without affecting the comfort level of occupants in commercial buildings. There are two types of agents which are room agents and proxy agents. Proxy agents represent occupants through containing occupant's model, and they interact with Room agents on behalf of occupants. Room agents represent the offices or conference rooms, and their task is to reduce energy consumption in the represented room through negotiating with Proxy agents to minimise the use of office devices (lights, HVAC, etc.) or relocating meetings to more efficient smaller rooms. An interesting point is that irritation levels are examined in the study (irritation might be occur when an occupant gets a message by her Proxy agent frequently). According to examination, a simple message might reach much higher irritation levels than a complex message when the frequency of message sending is increased.
- Automation in Construction
Multi-agent comfort and energy system (MACES) provides an alternative model to manage and control building devices and occupants considering actual thermal zones, temperatures, occupant preferences, and occupant schedules. There are three types of agents which are device agents, human agents and meeting agents. Device agents represent HVAC, lighting and appliance agents. They mainly monitor and control sensors and switches. Human agents represent permanent and temporary occupants who possess different behaviours, schedules and preferences. The simulation of the system utilise four control strategies which are baseline, reactive, proactive and proactive-MDP. The control capabilities varies for each strategy from just adjusting temperature and lighting to relocating meetings. It is stated that proactive-MDP strategy in which agents additionally can change meeting schedules outperforms other strategies in terms of energy savings and comfort levels.
- A Multi-Sensor Based Occupancy Estimation Model for Supporting Demand Driven HVAC Operations
This paper focuses on the estimating the occupancy in buildings so as to adjust HVAC operations. The estimation model is built on several sensors. Each sensor node includes a light sensor, a sound sensor, a motion sensor, a humidity sensor, a CO2 sensor, a temperature sensor and a PIR sensor. To estimate the number of occupants, sensor data is processed through radial basis function network. The overall detection rate is 87.62% for self-estimation and 64.83% for cross-estimation. The sensor node costs about $230 USD.
The following papers basically repeat the work explained in the above. However, they provide some brief descriptions of calculations utilised in the simulation.
- SAVES: A Sustainable Multi-Agent Application to Conserve Building Energy Considering Occupants
- Towards Optimal Planning for Distributed Coordination Under Uncertainty in Energy Domains
The aim of the work is to maximise energy savings without affecting the comfort level of occupants in commercial buildings. There are two types of agents which are room agents and proxy agents. Proxy agents represent occupants through containing occupant's model, and they interact with Room agents on behalf of occupants. Room agents represent the offices or conference rooms, and their task is to reduce energy consumption in the represented room through negotiating with Proxy agents to minimise the use of office devices (lights, HVAC, etc.) or relocating meetings to more efficient smaller rooms. An interesting point is that irritation levels are examined in the study (irritation might be occur when an occupant gets a message by her Proxy agent frequently). According to examination, a simple message might reach much higher irritation levels than a complex message when the frequency of message sending is increased.
- Automation in Construction
Multi-agent comfort and energy system (MACES) provides an alternative model to manage and control building devices and occupants considering actual thermal zones, temperatures, occupant preferences, and occupant schedules. There are three types of agents which are device agents, human agents and meeting agents. Device agents represent HVAC, lighting and appliance agents. They mainly monitor and control sensors and switches. Human agents represent permanent and temporary occupants who possess different behaviours, schedules and preferences. The simulation of the system utilise four control strategies which are baseline, reactive, proactive and proactive-MDP. The control capabilities varies for each strategy from just adjusting temperature and lighting to relocating meetings. It is stated that proactive-MDP strategy in which agents additionally can change meeting schedules outperforms other strategies in terms of energy savings and comfort levels.
- A Multi-Sensor Based Occupancy Estimation Model for Supporting Demand Driven HVAC Operations
This paper focuses on the estimating the occupancy in buildings so as to adjust HVAC operations. The estimation model is built on several sensors. Each sensor node includes a light sensor, a sound sensor, a motion sensor, a humidity sensor, a CO2 sensor, a temperature sensor and a PIR sensor. To estimate the number of occupants, sensor data is processed through radial basis function network. The overall detection rate is 87.62% for self-estimation and 64.83% for cross-estimation. The sensor node costs about $230 USD.
The following papers basically repeat the work explained in the above. However, they provide some brief descriptions of calculations utilised in the simulation.
- SAVES: A Sustainable Multi-Agent Application to Conserve Building Energy Considering Occupants
- Towards Optimal Planning for Distributed Coordination Under Uncertainty in Energy Domains
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