EMBEDDED DEEP COMPRESSION FOR TIME-SERIES DATA

    公开(公告)号:US20220190842A1

    公开(公告)日:2022-06-16

    申请号:US17439836

    申请日:2019-03-22

    IPC分类号: H03M7/30 G06N3/08

    摘要: A lossy compression algorithm is described for performing data compression of high-frequency floating point time-series data, for example. The compression algorithm utilizes a prediction engine that employs at least one of a linear prediction model or a non-linear prediction model to calculate one-step-ahead prediction of a current data value at current sampling time t using N previous quantized data values, where N is the model order. A prediction error is determined between the predicted value and an actual value, and the prediction error is quantized. A quantized current data value is determined from the predicted value and the quantized prediction error. The quantized prediction error is sent from an edge device to a data decompressor on a cloud device. The decompressor reconstructs the quantized current data value using the received quantized prediction error and by generating the same predicted value as the compressor.

    Social learning softthermostat for commercial buildings

    公开(公告)号:US09933796B2

    公开(公告)日:2018-04-03

    申请号:US14426851

    申请日:2013-09-12

    IPC分类号: G05D23/19 F24F11/00 G05B15/02

    摘要: A building has climate control equipment which controls a temperature at different locations. Different locations may be in different control zones controlled by different control devices. An occupant of a location submits a desired location temperature through a user interface on a computing device to a networked server. Setting of a desired temperature is constrained by energy saving policies and by conditions of surrounding locations. An arbitrator device determines based on constraints a new temperature setting. The new temperature setting is accompanied by an energy saving feedback. The occupants confirms the new setting. A climate control device is instructed to apply a device setting to achieve the new temperature. A climate profile of the occupant is learned from previous temperature settings by the occupant.

    Social Learning SoftThermostat for Commercial Buildings
    3.
    发明申请
    Social Learning SoftThermostat for Commercial Buildings 有权
    商业建筑学社会学习软石

    公开(公告)号:US20150247646A1

    公开(公告)日:2015-09-03

    申请号:US14426851

    申请日:2013-09-12

    IPC分类号: F24F11/00 G05B15/02

    摘要: A building has climate control equipment which controls a temperature at different locations. Different locations may be in different control zones controlled by different control devices. An occupant of a location submits a desired location temperature through a user interface on a computing device to a networked server. Setting of a desired temperature is constrained by energy saving policies and by conditions of surrounding locations. An arbitrator device determines based on constraints a new temperature setting. The new temperature setting is accompanied by an energy saving feedback. The occupants confirms the new setting. A climate control device is instructed to apply a device setting to achieve the new temperature. A climate profile of the occupant is learned from previous temperature settings by the occupant.

    摘要翻译: 一座建筑物具有控制不同地点温度的气候控制设备。 不同的位置可能位于由不同控制装置控制的不同控制区域中。 位置的乘客通过计算设备上的用户界面向联网的服务器提交期望的位置温度。 所需温度的设定受到节能政策和周边环境的限制。 仲裁器装置根据约束确定新的温度设置。 新的温度设置伴随着节能反馈。 乘客确认了新的环境。 指示气候控制装置施加装置设定以达到新的温度。 从乘客的以前的温度设置中了解到乘客的气候特征。