Abstract: Implementations of the disclosed technologies pre-fetch search results. Implementations receive first input from a search session of a user device, where the first input includes at least a portion of a search term but does not initiate a search. Implementations determine context data associated with the first input, determine that a combination of the first input and the context data satisfies a pre-fetch threshold, determine intent data based on at least a portion of the context data, generate a search query based on the first input and the intent data, and pre-fetch a first subset of search results based on the search query. In response to a second input received subsequent to the first input, where the second input contains an initiate search signal, implementations initiate rendering of the pre-fetched first subset of search results in the search session at the user device.

Abstract: Implementations of the disclosed technologies pre-fetch search results. Implementations receive first input from a search session of a user device, where the first input includes at least a portion of a search term but does not initiate a search. Implementations determine context data associated with the first input, determine that a combination of the first input and the context data satisfies a pre-fetch threshold, determine intent data based on at least a portion of the context data, generate a search query based on the first input and the intent data, and pre-fetch a first subset of search results based on the search query. In response to a second input received subsequent to the first input, where the second input contains an initiate search signal, implementations initiate rendering of the pre-fetched first subset of search results in the search session at the user device.

Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month.

Abstract: Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

Abstract: Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

Abstract: A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

Abstract: Systems and methods for demand charge minimized operations while extending battery life, including determining a demand charge threshold based on received historical data. The systems and methods further including generating a charging pattern for a battery array from the historical. The systems and methods further including calculating a charging schedule for the battery array based on the demand charge threshold, a short term load profile, and the charging pattern. The charging schedule being calculated to follow the charging pattern without exceeding the demand charge threshold and transmitting commands to a battery controller in accordance with to the charging schedule.

Abstract: A system and method are provided. The system includes a processor. The processor is configured to receive power related data relating to power usage of power consuming devices at a customer site from a plurality of sources. The processor is further configured to generate object function inputs from the power related data. The processor is additionally configured to apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site. The processor is also configured to initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system.

Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

Abstract: A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a forecast model from a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a monthly DCT value for the facility using the forecast model. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

Abstract: A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a next month DCT value for the facility using the data set of DCT values. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

Abstract: Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

Abstract: Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

Abstract: A system to manage a power grid includes one or more storage and generator devices coupled to the power grid; and a decentralized management module to control the devices including: a module to perform decentralized local forecasts; and a module to perform decentralized device reconfiguration.

Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month.

Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) to maximize battery lifetime, including determining optimal monthly demand charge thresholds based on a received customer load profile, battery manufacturer specifications, and battery operating conditions and parameters. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine battery utilization, and minimize demand charge for each day for the load profile. A battery lifetime is predicted based on manufacturer specifications and utilization determined by the daily optimizations. A battery capacity retention value and battery capacity loss are determined based on an annual discharged energy (AADE) and an average battery state-of-charge (SoC). An optimal monthly demand threshold is selected based on the predicted battery lifetime and demand charge utilization.

Abstract: A computer-implemented method is provided for controlling a Battery Energy Storage System (BESS) having a battery set and connected to a Photovoltaic (PV) panel set. The method includes enforcing, by a processor device, a multi-objective Model Predictive Control (MPC) optimization on the BESS. The multi-objective MPC optimization includes a first objective of reducing a possibility of Demand Charge Threshold violations by minimal DCT increments which provide a higher demand charge savings, a second objective of improving a robustness of the BESS against energy forecast errors by increasing a State Of Charge (SOC) of the battery set, and a third objective of maximizing PV-utilization. The method further includes controlling, by the processor device, charging and discharging of the BESS in accordance with the multi-objective MPC optimization to meet the first, second, and third objectives.

Abstract: Systems and methods for optimal sizing of one or more grid-scale batteries for frequency regulation service, including determining a desired battery output power for the one or more batteries for a particular period of time. A battery size is optimized for the one or more batteries for the particular period of time, and the optimizing is repeated using different time periods to generate a set of optimal battery sizes based on at least one of generated operational constraints or quality criteria constraints for the one or more batteries. A most optimal battery is selected from the set of optimal battery sizes.

Abstract: Systems and methods for adaptive demand charge management in a behind the meter energy management system. The system and method includes determining, in a first layer, an initial demand charge threshold (DCT), for a first period, based on historical DCT profiles, and generating recursively, in a second layer, a forecast of a power demand for a second period, wherein the second period is a subset of the first period. Further included is combining the first layer and the second layer to recursively modify the initial DCT with a DCT adjustment value to generate a modified DCT, wherein the DCT adjustment value is optimized according to the forecast of power demand for the second period, and controlling batteries according to the modified DCT, wherein the batteries are discharged if power demand is above the modified DCT, and the batteries are charged if the power demand is below the modified DCT.