公开(公告)号：US11747848B2

公开(公告)日：2023-09-05

申请号：US17103282

申请日：2020-11-24

Applicant: GridPoint, Inc.

Inventor： Danny K. Dyess ,  Arnie J. Tamagni ,  Mark W. Vinson

IPC: G05F1/66 ,  F24F11/30 ,  F24F11/64 ,  F24F11/47 ,  F24F11/62 ,  F24F11/65 ,  F24F11/56 ,  G05B15/02 ,  G06N5/04 ,  G05B19/048 ,  F24F11/63 ,  F24F11/46 ,  F24F140/00

CPC classification number: G05F1/66 ,  F24F11/30 ,  F24F11/47 ,  F24F11/56 ,  F24F11/62 ,  F24F11/63 ,  F24F11/64 ,  F24F11/65 ,  G05B15/02 ,  G05B19/048 ,  G06N5/04 ,  F24F11/46 ,  F24F2140/00 ,  G05B2219/2614

Abstract: A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

公开(公告)号：US09880577B2

公开(公告)日：2018-01-30

申请号：US14513374

申请日：2014-10-14

Applicant: GridPoint, Inc.

Inventor： Danny K. Dyess ,  Arnie J. Tamagni ,  Mark W. Vinson

IPC: H02J3/14 ,  G05F1/66 ,  G05B15/02 ,  G06N5/04 ,  G05B19/048 ,  F24F11/00

CPC classification number: G05F1/66 ,  F24F11/30 ,  F24F11/46 ,  F24F11/47 ,  F24F11/62 ,  F24F11/63 ,  F24F11/64 ,  G05B15/02 ,  G05B19/048 ,  G05B2219/2614 ,  G06N5/04 ,  H02J3/14

Abstract: A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

公开(公告)号：US20150142194A1

公开(公告)日：2015-05-21

申请号：US14513374

申请日：2014-10-14

Applicant: GridPoint, Inc.

Inventor： Danny K. Dyess ,  Arnie J. Tamagni ,  Mark W. Vinson

IPC: G05F1/66 ,  G06N5/04 ,  G05B15/02

CPC classification number: G05F1/66 ,  F24F11/30 ,  F24F11/46 ,  F24F11/47 ,  F24F11/62 ,  F24F11/63 ,  F24F11/64 ,  G05B15/02 ,  G05B19/048 ,  G05B2219/2614 ,  G06N5/04 ,  H02J3/14

Abstract: A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

Abstract translation: 公开了一种系统和方法，用于动态地学习建筑物的最佳能量消耗操作条件并且监视/控制能量消耗设备以将峰值需求间隔保持在最小。 所采用的动态需求限制算法采用两种单独的控制方案，一种用于HVAC负载，一种用于非HVAC负载。 单独的操作参数可以应用于两种类型的负载，并且可以配置多个非HVAC（控制区）负载。 该算法在其实时限制策略中使用历史峰值需求测量。 该算法不断尝试减少用户配置参数内的峰值需求。 当新的峰值不可避免时，该算法以限制新的峰值幅度并将操作条件置于用户配置参数内的方式策略性地去除和/或引入负载。 在一个实施例中，该算法检查之前七天的测量信息以识别峰值需求间隔。 系统然后使用实时负载信息预测即将到来的时间间隔的需求峰值，并策略性地调整分配的负载，以限制需求峰值，以便不设置新的峰值。