公开(公告)号：WO2018049504A1

公开(公告)日：2018-03-22

申请号：PCT/CA2017/000202

申请日：2017-09-12

Applicant: GREEN POWER LABS INC.

Inventor： PAVLOVSKI, Alexandre ,  ANICHKOV, Dmitriy

IPC: G06Q50/06 ,  G06Q30/02 ,  H02J13/00

Abstract: A method for establishing an energy clearing price for electrical energy within a microgrid is provided. The method comprising receiving, at a microgrid market management ("MMM") system, a price for electrical energy delivered to the microgrid from a transmission/distribution system or the transmission/distribution system from the microgrid; monitoring electric power flow between a first and a second prosumer systems and the microgrid using respective first and second prosumer meters located at respective locations of said first and second prosumer systems within the microgrid; monitoring electric power flow between the microgrid and the transmission/distribution system using a meter located at a point of common coupling ("PCC") between the microgrid and the transmission/distribution system; and determining, at the MMM, the energy clearing price from the price for electrical energy delivered to the microgrid from the transmission/distribution system or the transmission/distribution system from the microgrid; the respective locations of the first and second prosumer systems within the microgrid; and, the electric power flow between the first and second prosumer systems and the microgrid.

Abstract translation: 提供了用于建立微电网内的电能的能量清算价格的方法。 该方法包括在微电网市场管理（“MMM”）系统处接收从微电网从传输/分配系统或传输/分配系统传递到微电网的电能的价格; 使用位于所述微电网内的所述第一和第二消耗者系统的相应位置处的相应的第一和第二消耗计量表来监测第一和第二消费者系统与所述微电网之间的电力流; 使用位于微电网和传输/分配系统之间的公共耦合点（“PCC”）处的仪表来监测微电网和传输/分配系统之间的电力流; 并且在MMM处根据从微电网从传输/分配系统或传输/分配系统传递到微电网的电能的价格确定能量清算价格; 微电网内第一和第二消耗系统的相应位置; 以及第一和第二消费者系统与微电网之间的电力流动。

公开(公告)号：WO2018218334A1

公开(公告)日：2018-12-06

申请号：PCT/CA2018/000107

申请日：2018-05-30

Applicant: GREEN POWER LABS INC.

Inventor： LINGRAS, Pawan ,  PAVLOVSKI, Alexandre ,  PAVLOVSKI, Ilia ,  CALDWELL, Aaron ,  HILLIARD, Trent ,  QIN, Zheng

IPC: G06Q10/00

Abstract: A method for generating an AI-based building energy model for a client building, comprising: generating an energy profile database by: defining a set of building parameters; generating energy profiles by simulating a set of physical building models; and, populating the energy profile database with the energy profiles; determining an energy profile for the client building by: splitting the energy profile database into groups and clustering each group into a set of clusters; selecting a cluster; and, selecting the energy profile in the cluster that is a closest match to that of the client building; selecting a physical building model from a building model database that corresponds to the energy-profile; calibrating the physical building model to generate an adjusted building model; and, generating a set of training datasets from the adjusted building model and inputting the set of training datasets into an AI module to generate the AI-based model.

公开(公告)号：WO2016168910A1

公开(公告)日：2016-10-27

申请号：PCT/CA2016/000107

申请日：2016-04-11

Applicant: GREEN POWER LABS INC.

Inventor： PAVLOVSKI, Alexandre ,  QIN, Zheng ,  ANICHKOV, Dmitriy ,  FLETCHER, James

IPC: F24F11/053 ,  F24F11/00 ,  G05D23/19

CPC classification number: F24F11/30 ,  F24F11/46 ,  F24F11/62 ,  F24F11/64 ,  F24F11/65 ,  F24F2110/10 ,  F24F2110/12 ,  F24F2120/10 ,  F24F2120/12 ,  F24F2130/00 ,  F24F2130/10 ,  F24F2130/20 ,  F24F2140/50 ,  F24F2140/60 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/2642 ,  G05D23/1917 ,  G05D23/1934

Abstract: A method for controlling temperature in a thermal zone within a building, comprising: using a processor, receiving a desired temperature range for the thermal zone; determining a forecast ambient temperature value for an external surface of the building proximate the thermal zone; using a predictive model for the building, determining set points for a heating, ventilating, and air conditioning ("HVAC") system associated with the thermal zone that minimize energy use by the building; the desired temperature range and the forecast ambient temperature value being inputs to the predictive model; the predictive model being trained using respective historical measured value data for at least one of the inputs; and, controlling the HVAC system with the set points to maintain an actual temperature value of the thermal zone within the desired temperature range for the thermal zone.

Abstract translation: 一种用于控制建筑物内的热区域中的温度的方法，包括：使用处理器，接收所述热区域的期望温度范围; 确定靠近热区的建筑物外表面的预测环境温度值; 使用建筑物的预测模型，确定与热区相关联的加热，通风和空调（“HVAC”）系统的设定点，其最小化建筑物的能量使用; 期望的温度范围和预测环境温度值是预测模型的输入; 使用用于至少一个输入的相应历史测量值数据来训练所述预测模型; 并且利用设定点控制HVAC系统以将热区域的实际温度值保持在热区域的期望温度范围内。