Brown out peak prediction and control system for predicting a time when energy consumption on a grid will exceed normal production capacity

A system for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the system comprising:

• a building lag optimizer, configured to determine an energy lag for one of the buildings, said building lag optimizer comprising:
• a thermal response processor, configured to generate a plurality of time shifted versions of energy consumption data for said one of the buildings, each of said plurality of time shifted versions of energy consumption data comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said plurality of time shifted versions of energy consumption data 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
• a regression engine, coupled to said thermal response processor, configured to receive said plurality of time shifted versions, and configured to perform a regression analysis on said each of said plurality of time shifted versions of energy consumption data to yield corresponding regression model parameters and a corresponding residual;
• 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 one of the buildings, and wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature;
• a peak prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of the buildings in the grid, outside temperatures, said corresponding energy lag, and said corresponding regression model parameters, and configured to estimate a cumulative energy consumption for the buildings, and configured to predict the time when energy consumption on the grid will exceed normal production capacity;
• and
• peak control, coupled to said peak prediction element, configured to receive the time when energy consumption on the grid will exceed normal production capacity, and configured to prepare and commence exceptional measures required to manage the energy consumption.

A method for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the method comprising:

• generating a plurality of time shifted versions of energy consumption data for each of the buildings, each of the plurality of time shifted versions of energy consumption data comprising energy consumption values along with corresponding time and outside temperature values, wherein the energy consumption values within the each of the plurality of time shifted versions of energy consumption data are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and wherein each of the plurality of lag values is different from other ones of the plurality of lag values;
• performing a regression analysis on the each of the plurality of time shifted versions of energy consumption data to yield corresponding regression model parameters and a corresponding residual;
• determining a least valued residual from all residuals yielded by said performing a regression analysis, the least valued residual indicating a corresponding energy lag for the each of the buildings, wherein the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature;
• using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption on the grid will exceed normal production capacity;
• and
• receiving the time when energy consumption on the grid will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.