Simulation system and method for predicting heating and cooling load in building
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Solution Overview
Problem
Current building energy management systems struggle to accurately identify and manage peak energy demand, particularly in cooling and heating, leading to discomfort for occupants and reduced participation in demand management programs, as existing methods focus on power load control rather than precise load prediction and visualization.
Innovation Solution
A simulation system and method that collects real-time indoor and outdoor temperature, humidity, and insolation data using the radiant time series method to predict and visualize heating and cooling loads, allowing for changes in envelope performance and indoor set temperature, thereby enabling precise load prediction and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power load control is implemented to reduce cooling and heating power during peak demand, then energy consumption is reduced, but indoor temperature comfort deteriorates
Solution Approach 1:
The system performs preliminary identification of cooling and heating load sources by analyzing historical energy consumption data and environmental factors (temperature, humidity, insolation) to predict future load patterns. This allows proactive energy management before peak demand occurs, reducing the need for abrupt load control that would compromise comfort.
Solution Approach 2:
The system introduces an intermediary analysis layer that decomposes total energy consumption into specific cooling and heating load components. By using environmental data and load calculation models, the system identifies the precise sources and magnitudes of thermal loads, enabling targeted energy management strategies that address only the necessary cooling or heating requirements rather than applying blanket power control.
2Measurement precision
If detailed identification of energy use sources is implemented, then energy management precision is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional energy management platform that integrates multiple capabilities: data collection from various sensors, environmental monitoring, load calculation using standardized methods, data analysis, and visualization. This universal system handles diverse energy management tasks through a unified architecture, avoiding the need for separate specialized systems for each function.
Solution Approach 2:
The system combines previously separate functions into a unified energy management system. Environmental sensors, energy meters, load calculation algorithms, and visualization tools are merged into a single integrated platform that processes and analyzes data comprehensively, reducing the complexity that would arise from multiple independent systems while improving measurement precision.
3Measurement precision
If real-time environmental data collection is implemented, then load prediction accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts and focuses only on the critical environmental parameters that directly influence cooling and heating loads: outdoor temperature, outdoor humidity, and insolation. By selectively collecting only these essential data elements rather than monitoring all possible building parameters, the system achieves accurate load prediction while minimizing data processing requirements.
Solution Approach 2:
The system transforms raw environmental data into meaningful load indicators by applying load calculation methods that convert temperature, humidity, and insolation measurements into cooling and heating load values. This parameter transformation reduces the complexity of analyzing multiple raw environmental variables and focuses on the derived load parameters that directly inform energy management decisions.
Data Source
AI summary
The simulation system for predicting heating and cooling loads of a building comprises the collection unit collecting measurement data of a target building from a BEMS, the identification unit identifying indoor and outdoor temperature, humidity, and insolation measured in the building in real time based on RTS method, the correlation derivation unit deriving a correlation between energy usage based on the BEMS measurement data collected by the collection unit and the cooling and heating loads according to indoor and outdoor temperature, humidity, and insolation identified by the identification unit, the simulation unit predicting a change in cooling and heating loads according to a change in at least one of pieces of measurement data based on the correlation derived from the correlation derivation unit to perform a simulation, and the information provision unit providing a simulation result from the simulation unit in a visible form to a user terminal.