PATENT CLAIM ANALYSIS

Application Number: 15961073
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-08
Patent Classification: ["700", "291000"]

Abstract:
A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, wherei the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a machine learning model analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

Claim (Index 8):
A system for predicting a dispatch for buildings participating in a demand response program event, the system comprising:\n a building lag optimizer, configured to determine an energy lag for a building, said building lag optimizer comprising:\n a thermal response processor, configured to generate a plurality of energy use data sets for said building, 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 analysis 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 regression engine, said least valued residual indicating said energy lag for said building, wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature; \n a dispatch prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of the buildings, outside temperatures, said corresponding energy lag, and said corresponding machine learning model parameters, and configured to estimate a cumulative energy consumption for the buildings, and configured to predict a dispatch order reception time for the demand response program event; and a dispatch control element, coupled to said dispatch prediction element, configured to receive said dispatch order reception time, and configured to prepare actions required to control said each of the buildings to optimally shed energy specified in a corresponding dispatch order.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 91.0
- Lexical Diversity: 2.11538
- Patent Class: 700.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15903596', '14984785', '15903533', '14674021', '14984706']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5040245217934299
- 35 USC 102 Novelty (BERT): 0.5894757029644973
- Combined Prediction Score: 0.5125696399105367
- Mean Citation Score: 504.50663600000007
- Max Citation Score: 507.22586
- Similarity Product: 475.19155962866313

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