PATENT CLAIM ANALYSIS

Application Number: 15903705
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["700", "291000"]

Abstract:
A method for dispatching buildings in a demand response program event including generating data sets for each of the buildings, each set having energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each of the plurality of lag values is different from other ones of the plurality of lag values; performing a machine learning model analysis on each set to yield machine learning model parameters and a residual; determining a least valued residual from all residuals yielded, the least valued residual indicating a corresponding energy lag for the each of the buildings; and using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags.

Claim (Index 8):
A system for dispatching buildings participating in a demand response program event, the system comprising:\n baseline data stores, configured to store a plurality of baseline energy use data sets for the buildings; a building lag optimizer, configured to determine an energy lag for one of said buildings, said building lag optimizer comprising:\n a thermal response processor, configured to generate a plurality of energy use data sets for said one of the buildings, each of said plurality of energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said plurality of energy use data sets are shifted by one of a plurality of lag values relative to said corresponding time and outside temperature values, and wherein each of said plurality of lag values is different from other ones of said plurality of lag values; and \n a machine learning model engine, coupled to said thermal response processor, configured to receive said plurality of energy use data sets, and configured to perform a machine learning model analysis on said each of said plurality of energy use data sets to yield corresponding machine learning model parameters and a corresponding residual; \n wherein said thermal response processor determines a least valued residual from all residuals yielded by said machine learning model engine, said least valued residual indicating said energy lag for said one of the buildings, wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature; \n a dispatch processor, coupled to said building lag optimizer, configured to receive a plurality of energy lags, each corresponding to one of the buildings, and configured to generate a dispatch schedule for the demand response program event according to a prioritization of said plurality of energy lags; and a dispatch control element, coupled to said dispatch processor, configured to control a specified load shedding by performing load shedding actions in the order provided by said dispatch schedule, wherein said dispatch schedule directs for dispatch of ones of the buildings having greater energy lags prior to others of the buildings having lesser energy lags.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 88.0
- Lexical Diversity: 2.23944
- Patent Class: 700.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14984612', '14984706', '14674021', '15903596', '15903651']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5005965799341289
- 35 USC 102 Novelty (BERT): 0.5944427111047629
- Combined Prediction Score: 0.5099811930511923
- Mean Citation Score: 502.31855
- Max Citation Score: 516.7887
- Similarity Product: 487.41227277054185

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test