Abstract: A technique for energy sample forecasting of heating, venting and air conditioning-refrigeration (HVAC-R) systems is disclosed. In an example, a first expected energy sample of a HVAC-R system at a first time period is forecasted by modelling actual energy samples of the HVAC-R system at previous time periods using a statistically-based seasonal-autoregressive integrated moving average (SARIMA) model. Further, an anomaly is detected at the first time period when deviation between the first expected energy sample and an actual energy sample at the first time period is greater than a dynamic context sensitive threshold. Also, an expected energy sample at next time period is forecasted by modelling a second expected energy sample of the HVAC-R system at the first time period using the statistically-based SARIMA model upon detecting anomaly. The second expected energy sample is forecasted by modelling the actual energy samples at the previous time periods using a physical model.

Abstract: Method and system for predicting temporal-spatial distribution of load demand on an electric grid due to a plurality of Electric Vehicles (EVs) is described. The method includes creating an EV load demand (EVLD) model for a Region of Interest (ROI) serviced by the electric grid, wherein the EVLD model integrates an EV model and a transport simulator simulating EV traffic conditions for the ROI. Further, the method includes computing the load demand in time and space in terms of State of Charge (SOC) of a battery for each EV among the plurality of EVs in the ROI, based on the EVLD model. Furthermore, the method includes aggregating the computed the load demand, in terms of the SOC, of each EV in time domain and space domain to create a temporal-spatial impact of the load demand by the plurality of EVs on the electric grid for the ROI.

Abstract: Devices and methods comprise determining an actual weather independent energy consumption value and actual weather dependent energy consumption value of a building. The devices and methods further comprise, obtaining energy simulation information from a data source. Further, an ideal weather independent energy consumption value and one or more ideal loads are generated based on the energy simulation information, building information, and total energy consumption of the building. The devices and methods further comprise determining a benchmarking score for the building using the actual weather independent energy consumption value, the actual weather dependent energy consumption value, the ideal weather independent energy consumption value, and the one or more ideal loads for managing energy consumption associated with the building.

Abstract: A system and method for optimizing energy consumption in a plurality of air handling units (AHUs) in a zone is provided. The system comprising a zone thermal unit that is configured to obtain a first set of input parameters including an internal heat gains, a surface convective loads, an intra-zone mixing, a supply air temperature, a second set of input parameters including internal moisture gains, a supply humidity ratio, and a third set of input parameters including an air contaminant concentration and an ambient contaminant concentration of the AHUs and generates a first set of output parameters including a zone temperature, a humidity ratio and an air concentration. The system further includes an optimizer that is configured to generate a second set of output parameters including an optimum combination of AHU flow rates for the AHUs based on at least one of the first set of output parameters.

Abstract: Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.

Abstract: Described herein, are methods and systems for locating a leak in a water distribution network. According to an implementation, a leak situation in the water distribution network is detected based on a flow difference value between an actual flow value and a predicted flow value of an inlet flow meter of the water distribution network at at least one time interval. Leak signature values of demand nodes in the water distribution network at the at least one time interval are determined. A leak signature value of a respective demand node at a respective time interval is determined based on centrality metrics, the predicted flow value at the respective time interval, and static physical properties related to the water distribution network. At least one possible leak node is identified based on the flow difference value and the leak signature values of the demand nodes at the at least one time interval.

Abstract: Electrical utilities offer incentives to customers to reduce consumption during periods of demand-supply mismatch. A building's participation in demand response (DR) depends both on its ability (due to building constraints), and its willingness (a function of incentive) to reduce electricity. Customers prefer a large incentive whereas a utility would want to minimize the revenue outflow to achieve a target reduction. Systems and methods of the present disclosure identify optimal incentive from the utility's perspective reflecting this trade-off. A model is built to estimate the demand response potential (DRP) of a building for a given incentive offered by the utility. The models for individual buildings are used to characterize the behavior of an ensemble of buildings. The utility may then decide optimum incentives that should be offered to achieve a target DR, using the associated DRP.

Abstract: Conventional systems for monitoring pipe networks are generally not scalable, impractical in the field with uncontrolled environments or rely of static features of pipes that are vary depending on the pipes under consideration. The ideal sensor-ed monitoring systems are not economically viable. Systems and methods of the present disclosure provide an improved data-driven model to rank pipes in the order of burst probabilities, by including dynamic feature values of pipes such as pressure and flow that depends on network structure and operations. The present disclosure enables estimating approximate values for the dynamic features since they are hard to estimate accurately in the absence of a calibrated hydraulic model. The present disclosure also validates the estimated approximate dynamic feature values for the purpose of estimating bursts likelihood vis-a-vis accurate values of the dynamic metrics.

Abstract: The subject matter described herein is directed to a power monitoring system for managing power in a data center. In one embodiment, the power monitoring system includes: at least one processor, a memory coupled to the at least one processor, wherein the memory includes, a computation module configured to compute at least one of peak power consumption and current power consumption of each power distribution point of a power distribution unit and an analysis module configured to identify the power distribution points which are overloaded or are underutilized based on a policy data.

Abstract: A technique for energy sample forecasting of heating, venting and air conditioning-refrigeration (HVAC-R) systems is disclosed. In an example, a first expected energy sample of a HVAC-R system at a first time period is forecasted by modelling actual energy samples of the HVAC-R system at previous time periods using a statistically-based seasonal-autoregressive integrated moving average (SARIMA) model. Further, an anomaly is detected at the first time period when deviation between the first expected energy sample and an actual energy sample at the first time period is greater than a dynamic context sensitive threshold. Also, an expected energy sample at next time period is forecasted by modelling a second expected energy sample of the HVAC-R system at the first time period using the statistically-based SARIMA model upon detecting anomaly. The second expected energy sample is forecasted by modelling the actual energy samples at the previous time periods using a physical model.

Abstract: A system and method for optimizing energy consumption in a plurality of air handling units (AHUs) in a zone is provided. The system comprising a zone thermal unit that is configured to obtain a first set of input parameters including an internal heat gains, a surface convective loads, an intra-zone mixing, a supply air temperature, a second set of input parameters including internal moisture gains, a supply humidity ratio, and a third set of input parameters including an air contaminant concentration and an ambient contaminant concentration of the AHUs and generates a first set of output parameters including a zone temperature, a humidity ratio and an air concentration. The system further includes an optimizer that is configured to generate a second set of output parameters including an optimum combination of AHU flow rates for the AHUs based on at least one of the first set of output parameters.

Abstract: Devices and methods comprise determining an actual weather independent energy consumption value and actual weather dependent energy consumption value of a building. The devices and methods further comprise, obtaining energy simulation information from a data source. Further, an ideal weather independent energy consumption value and one or more ideal loads are generated based on the energy simulation information, building information, and total energy consumption of the building. The devices and methods further comprise determining a benchmarking score for the building using the actual weather independent energy consumption value, the actual weather dependent energy consumption value, the ideal weather independent energy consumption value, and the one or more ideal loads for managing energy consumption associated with the building.

Abstract: Described herein, are methods and systems for locating a leak in a water distribution network. According to an implementation, a leak situation in the water distribution network is detected based on a flow difference value between an actual flow value and a predicted flow value of an inlet flow meter of the water distribution network at at least one time interval. Leak signature values of demand nodes in the water distribution network at the at least one time interval are determined. A leak signature value of a respective demand node at a respective time interval is determined based on centrality metrics, the predicted flow value at the respective time interval, and static physical properties related to the water distribution network. At least one possible leak node is identified based on the flow difference value and the leak signature values of the demand nodes at the at least one time interval.

Abstract: The subject matter described herein relates to a system and a method for generation of energy consumption profiles corresponding to a plurality of computing systems. For each of the plurality of the computing systems, a plurality of consumption parameters from at least one measurement device is received. The consumption parameters include a processor utilization parameter and an energy consumption parameter. Further, a normalization factor corresponding to each of the plurality of the computing systems is identified. Based on the normalization factor, the processor utilization parameter is normalized. Based on the normalized processor utilization parameter and the energy consumption parameter, the energy consumption profile is generated. The energy consumption profile is indicative of energy efficiency of the plurality of the computing systems.

Abstract: The subject matter described herein is directed to a power monitoring system for managing power in a data center. In one embodiment, the power monitoring system includes: at least one processor, a memory coupled to the at least one processor, wherein the memory includes, a computation module configured to compute at least one of peak power consumption and current power consumption of each power distribution point of a power distribution unit and an analysis module configured to identify the power distribution points which are overloaded or are underutilized based on a policy data.

Abstract: The subject matter described herein relates to a system and a method for generation of energy consumption profiles corresponding to a plurality of computing systems. For each of the plurality of the computing systems, a plurality of consumption parameters from at least one measurement device is received. The consumption parameters include a processor utilization parameter and an energy consumption parameter. Further, a normalization factor corresponding to each of the plurality of the computing systems is identified. Based on the normalization factor, the processor utilization parameter is normalized. Based on the normalized processor utilization parameter and the energy consumption parameter, the energy consumption profile is generated. The energy consumption profile is indicative of energy efficiency of the plurality of the computing systems.

Abstract: A method and system for analytically simulating the state of a 802.11 WLAN network having a plurality of stations at discreet timesteps, including analytically generating a plurality of sample paths of a network state, including obtaining probability distribution values for aspects of the network. The probability distributions may be precomputed and stored in a cache to be accessed during a simulation of a network.

Abstract: A method and system for analytically simulating the state of a 802.11 WLAN network having a plurality of stations at discreet timesteps, including analytically generating a plurality of sample paths of a network state, including obtaining probability distribution values for aspects of the network. The probability distributions may be precomputed and stored in a cache to be accessed during a simulation of a network.