US-20260127326-A1 - 3-DIMENSIONAL FLIGHT PLAN OPTIMIZATION ENGINE FOR BUILDING ENERGY MODELING

US20260127326A1US 20260127326 A1US20260127326 A1US 20260127326A1US-20260127326-A1

Abstract

Embodiments describe a computer-implemented method for generating a flight plan for a remote deployable transient sensory system using a coverage path planning system. The method includes generating, using the remote deployable transient sensory system, a first sensory dataset comprising sensory data associated with a plurality of building envelope features associated with a built environment, building a second sensory dataset comprising a 3-dimensional (3-D) point cloud model using the first sensory dataset and identifying, in the 3-D point cloud, via the processor, a plurality of virtual energy efficiency features associated with respective energy efficiency feature locations of the building. The system generates the flight plan based on a flight metric optimization scheme.

Inventors

Ramtin MOTAHAR
Simon Ignacio Briceno
Younghoon CHOI
Youngjun Choi
Jacob Barnett Stickney

Assignees

Joulea, Inc

Dates

Publication Date: 20260507
Application Date: 20251229

Claims (20)

1 . A method for using a travel plan in deploying a remote transient sensor system in a built environment and generating a continuously calibrated building energy model during building energy survey comprising: generating a travel plan for deploying a remote transient sensory system in a built environment, where the remote transient sensory system is capable of collecting sensory data indicative of a build envelope in the built environment; deploying the remote transient sensory system in the built environment according to the travel plan; collecting sensory data indicative of the build envelope with the remote transient sensory system; generating a building energy model using data including the sensory data; identifying one or more features that affect energy efficiency of the build envelope; updating the travel plan for deploying the remote transient sensory system so as to include one or more additional missions to collect additional sensory data indicative of the one or more energy efficiency affecting features; deploying the remote transient sensory system in the built environment according to the updated travel plan; collecting additional sensory data indicative of the building envelope with the remote transient sensory system; and continuously calibrating the building energy model at predetermined periods of time using data including the additional sensory data.
2 . The method according to claim 1 , further comprises providing an initial 3-D model of the building envelope prior to collecting the sensory data; wherein the initial 3-D model is employed in making the travel plan; updating the 3-D-model using data including the sensory data prior to creating the building energy model; wherein the 3-D model is employed in updating the travel plan; further updating the 3-D model using the additional sensory data, wherein the 3-D model is used in one or more of creating the building energy model, calibrating the building energy model and identifying the building energy efficiency affecting features.
3 . The method according to claim 2 , wherein the 3-D model is a point cloud model.
4 . The method according to claim 1 , wherein the remote transient sensory system is an unmanned aerial system, and the travel plan is a flight path.
5 . The method according to claim 4 , wherein the wherein the flight path is based on a metric optimization scheme.
6 . The method according to claim 5 , wherein the flight path optimizes one or more flight metric objectives comprising flight fuel usage, flight time, flight distance, and/or flight trajectory change.
7 . The method according to claim 5 , the flight path is generated by a coverage path planning system.
8 . The method according to claim 1 , wherein identifying one or more features that affect energy efficiency of the build envelope comprises the use of an artificial intelligence (AI) engine.
9 . The method according to claim 8 , wherein the artificial intelligence (AI) engine identifies a candidate source cause of an energy loss characteristic of the building envelope.
10 . The method according to claim 8 , further wherein the travel plan comprises a plurality of waypoints proximate to the one or more of the building energy efficiency affecting features.
11 . The method according to claim 1 , wherein the data used in continuously calibrating the building energy model further comprises one or more of pre-existing datasets associated with design data, sensory energy data, and/or construction data for the structure, real-time building operation data, and occupant data for the structure.
12 . The method according to claim 1 further comprises providing a mitigation recommendation to reduce energy loss associated with the energy efficiency affecting feature.
13 . A method for using a travel plan in deploying a remote transient sensor system in a built environment for generating a time lapse comparison of a building energy model comprising: providing a 3-D model representing a building envelope in the built environment, generating a travel plan for deploying a remote transient sensory system in a built environment, where the remote transient sensory system is capable of collecting sensory data indicative of the build envelope; deploying the remote transient sensory system in the built environment according to the travel plan; collecting sensory data indicative of the build envelope with the remote transient sensory system; generating a building energy model using data including the sensory data; deploying the remote transient sensory system in the built environment for the one or more additional missions, with each mission following a travel plan; collecting additional sensory data indicative of the building envelope with the remote transient sensory system during the one or more additional missions; and generating a time lapse comparison of the building energy model employing data including the additional sensory data.
14 . The method according to claim 13 wherein the one or more additional missions occur monthly, quarterly, semi-annually or annually.
15 . A system for creating a continuous calibrated building energy model utilizing metric optimization path planning, comprising: a remote transient sensory system capable of collecting sensory data indicative of a build envelope; means for generating a travel plan for the remote transient sensory system; means for deploying the remote transient sensory system in accordance with the travel plan to collect sensory data indicative of a build envelope; means for generating a building energy model from data including the sensory data; means for identifying one or more building energy efficiency affecting features of the build envelope; means for updating the travel plan from data including the sensory data; means for deploying the remote transient sensory system for one or more additional missions in the built environment during a predetermined period of time, for collecting additional sensory data indicative of the building envelope energy affecting feature of the structure; and means for collecting additional data indicative of the building envelope, the additional data including the additional sensory data collected during the one or more additional missions of the remote transient sensory system.
16 . The system according to claim 15 where the predetermined period of time is a building energy survey, the system further comprising: means for generating a continuously calibrated building energy model at predetermined periods of time using data including additional sensory data from the additional missions of the remote transient sensory system.
17 . The system according to claim 15 wherein the predetermined period of time is a year, the system further comprising: means for providing a 3-D model of the building envelope feature; and means for providing a time-lapse comparison of the building energy model based on data including data from the additional missions of the remote transient sensory system.
18 . The system according to claim 15 further comprising means for providing an initial 3-D model of the build envelope prior to collecting the sensory data; wherein the initial 3-D model is employed in making the travel plan; means for updating the 3-D-model using data including the sensory data prior to creating the building energy model; wherein the 3-D model is employed in updating the travel plan; means for further updating the 3-D model using data including the additional sensory data, wherein the updated 3-D model is used in one or more of means for generating the building energy model, means for continuously calibrating the building energy model and means for identifying the building energy efficiency affecting features.
19 . The system according to claim 15 , wherein the transient sensory system is an unmanned aerial system, and the travel plan is a flight path.
20 . The system according to claim 15 , wherein the means for identifying one or more building energy efficiency affecting features comprises an artificial intelligence (AI) engine.

Description

TECHNICAL FIELD The present disclosure relates to a system and method for improving the built environment design, construction and operation. More specifically, this disclosure relates to a system and method for active monitoring and energy usage quantification associated with a built environment during construction and post-occupancy using an aerial remote deployable transient sensory system and other available datasets including the built environment sensory energy data, and the built environment design information, among other data. BACKGROUND Cities, towns, businesses, and individuals seek out ways to be more sustainable. Most sustainability initiatives target a reduction in the use of energy or other resources. For most initiatives, the first step requires an understanding of where waste is occurring, and for large projects this is often a resource use study or energy consumption study. While energy consumption studies look at the ultimate resource use of the built environment, most prefer an immediate solution to reduce energy consumption for the party commissioning the study. Large scale evaluations, such as the one conducted in 2016 by Siemens in San Francisco using their City Performance Tool (CyPT), may evaluate resource use across a city and look for ways to improve energy consumption. This type of large-scale resource evaluation often guides a cost benefit analysis of immediate versus long term changes to reduce energy consumption. Until recently, the energy performance gap between modelled resource use and actual operational use was difficult to monitor because of the siloed nature of the industry. In other circumstances, the performance gap may be difficult to comprehend once modelled. Recent developments in automated building meters and other monitoring devices have improved identification and comprehension of the energy performance gap for owners and building operators. Resource analysis for new construction is generally accomplished using building energy models (BEMs). BEMs are computer generated models that are used to predict the post-occupancy resource usage of the built physical environment. BEMs such as EnergyPlus®, Integrated Environmental Solutions® (IES) and eQuest®, are computer-based software building simulation tools that focus on resource consumption, utility bills, and energy costs of various resource related items such as heating, ventilation and air conditioning (HVAC), lighting, and water consumption. While these models may address more than energy, they are nonetheless typically referred to as energy models. A typical energy model has inputs for location data such as physical geographical location, weather conditions, building orientation, and other pertinent site features; building envelope, such as air infiltration goals, area orientation, glazing, solar absorbance and visible light transmittance; internal gains such as lighting, plug loads, sensible and latent loads from occupants; schedules such as occupancy data; and various types of energy systems such as water heating systems, alternative energy types such solar and wind, types of space heating, cooling, ventilating, fan and pump types and other aspects of HVAC. BEMs have been available in the Architectural, Engineering, Construction & Operation (“AECO”) industry for many years, but they are often underutilized. BEMs are most often used near the end of the design phase to verify that the designed built environment will have the desired post-occupancy resource footprint once built. Outside of high-performance built environments or buildings seeking certifications such as Leadership in Energy and Environmental Design (LEED), Living Building Challenge, etc., BEMs are seldom considered past the initial design phase to guide design. Furthermore, the need to estimate the inputs and parameters employed by the BEMs creates discrepancies between the predicted and the actual resource performance. Consequently, each of the (1) design, (2) construction, and (3) operation phases are currently executed without an accurate reference basis (i.e., data and models), leading to discrepancies between the initial estimates of the built environment resource usage in the design phase and actual operation of the built environment post construction. These discrepancies from the BEMs can often be on the order of 20% to 50% less than actual post occupancy resource use. The sustainable commercial building community has recognized this problem. Consequently, standards such as LEED v4 and Living Building Challenge 4.0 are adding emphasis on commercial building post-occupancy performance verification. Unfortunately, these types of built environments are a small subset of new construction projects and an even smaller subset of the building stock and so these discrepancies continue to exist. Along with underutilization of BEMs, the construction industry has been slow to adopt other technologies for reducing energy costs, which has resulted in contin