Abstract:
Systems and devices for, and methods of, energy management via prompted response options based on detected anomalous conditions.

Description:
TECHNICAL FIELD 
     Embodiments pertain to systems and devices for, and methods of, energy management via prompted response options based on detected anomalous conditions. 
     BACKGROUND 
     Energy consuming devices such as regulated devices including air conditioners, freezers, air handling systems and water heaters vary their respective levels of consumption due to seasonal variations but the levels of consumption may also be affected by unusual or out of the norm conditions. Residential solar panels and wind-based electrical power generating systems generate energy and site energy storage devices generate energy and store energy respectively due to seasonal variations, but the levels of energy production and storage may also be affected by unusual or out of the norm conditions. 
     SUMMARY 
     Embodiments include methods, systems, and devices where, for example a method embodiment of energy management at a site may include the steps of: (a) associating, by a computing device, a detected anomalous energy state occurrence with a set of one or more contextual attributes of the site; and (b) providing, based on the set of one or more contextual attributes, a set of proposed responses per each member of a set of proposed causal situations. Optionally, the anomalous energy state is at least one of: anomalous energy consumption and anomalous energy production. Method embodiments may further comprise: revising the set of proposed causal situations based on an input designation of a proposed response. 
     Some embodiments may include the step of providing, based on the set of one or more contextual attributes, the set of proposed responses per each member of the set of proposed causal situations via a user interface for input designation. Method embodiments may further comprise: revising the set of proposed responses of at least one member of the set of proposed causal situations based on an input designation of a proposed response. In some embodiments the set of proposed causal situations may be provided in a ranked order based on likelihood estimates per each member of the set of proposed causal situations. Some embodiments may include the step of: applying a weighting factor to at least one member of the set of proposed causal situations, and where the set of proposed causal situations is provided in a ranked order based on weighted likelihood estimates. Optionally, the weighting factor is based on a frequency of input designation of a proposed response. Some embodiments may further include the step of: applying a weighting factor to at least one member of the set of proposed responses, and where the set of proposed responses is provided in a ranked order based on numerical weight. Optionally, the weighting factor is based on a frequency of input designation of a proposed response. In other embodiments, a detected anomalous energy consumption occurrence is based on a comparison of estimated energy consumption and expected energy consumption based on the set of one or more contextual attributes of the site. Optionally, the set of one or more contextual attributes are derived from a simulated diurnal-seasonal model of the site. 
     Embodiments pertain to devices for energy management at a site, and the device embodiment may comprise: (a) a processor, and (b) an addressable memory, where the addressable memory comprises a set of one or more contextual attributes of the site, and where the processor is configured to: (a) associate a detected anomalous energy state occurrence with the set of one or more contextual attributes of the site, and (b) provide, based on the set of one or more contextual attributes, a set of proposed responses per each member of a set of proposed causal situations. In some embodiments, the anomalous energy state is at least one of: anomalous energy consumption and anomalous energy production. In some embodiments, the processor may be further configured to perform the step of revising the set of proposed causal situations based on an input designation of a proposed response. In some embodiments, the processor may be further configured to provide, based on the set of one or more contextual attributes, the set of proposed responses per each member of the set of proposed causal situations, via a user interface for input designation. In other embodiments, the processor may be further configured to revise the set of proposed responses of at least one member of the set of proposed causal situations based on an input designation of a proposed response. In another embodiment, the processor may be further configured to provide the set of proposed causal situations in a ranked order based on likelihood estimates per each member of the set of proposed causal situations. In other embodiments, the processor is further configured to apply a weighting factor to at least one member of the set of proposed causal situations, and where the processor is further configured to provide the set of proposed causal situations in a ranked order based on weighted likelihood estimates. Optionally, the weighting factor is based on a frequency of input designation of a proposed response. In other embodiments, the processor is further configured to apply a weighting factor to at least one member of the set of proposed responses, and where the processor is further configured to provide a set of proposed responses in a ranked order based on numerical weight. Optionally, the weighting factor is based on a frequency of input designation of a proposed response. In other embodiments, a detected anomalous energy consumption occurrence is based on a comparison of estimated energy consumption and expected energy consumption based on the set of one or more contextual attributes of the site. Optionally, the processor is further configured to derive the set of one or more contextual attributes from a simulated diurnal-seasonal model of the site. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, and in which: 
         FIG. 1  is a functional block diagram depicting an exemplary system embodiment; 
         FIG. 2  is a functional block diagram depicting an exemplary computing device embodiment; 
         FIG. 3  is a functional block diagram depicting an exemplary system embodiment; 
         FIG. 4  depicts a functional block diagram of a portion of an exemplary system embodiment; 
         FIG. 5  is a top level flowchart depicting an exemplary process embodiment; 
         FIG. 6  is a functional block diagram depicting an exemplary system embodiment; 
         FIG. 7  depicts a functional block diagram of a portion of an exemplary system embodiment; and 
         FIG. 8  is a top level flowchart depicting an exemplary process embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a functional block diagram depicting an exemplary system embodiment  100  comprising one or more home networked devices  110  that may be sited within a residence  111  and optionally interconnected with offsite processing  120  via one or more network links, e.g., the Internet  130 . An array of home networked devices  110  is depicted in  FIG. 1  as including a home energy management system (HEMS)  140 , a user interface  150 , and one or more energy-consuming devices or appliances  161 - 163 . The home energy management system  140 , of the array of home networked devices  110 , may monitor the one or more energy-consuming devices or appliances  161 - 163 , and may effect operational changes to one or more energy-consuming devices or appliances  161 - 163 . The home energy management system  140  may monitor one more environmental conditions, such as an air temperature of the residence  111 , via a sensor  170 , such as an air temperature sensor. 
     The home energy management system  140  may be configured as a computing device.  FIG. 2  is a functional block diagram of an exemplary computing device  220  having a processor  224  and memory  227  addressable via a data bus  228 . A user interface  229 , a power source interface  221 , and a network interface  226  by which one or more local devices, and/or internet sites such as an offsite processor, may communicate with the processor  224  via the data bus  228 . The processor  224  may be configured to execute programmed steps via a real-time operating system  225  where the steps that comprise the application  222  may include energy consumption measurements that are taken or are estimated, effecting the weighting and ranking of both potential causal situations and associated potential reactions to the causal situations, and generating device control signals and user feedback. 
       FIG. 3  is a functional block diagram depicting another exemplary system embodiment  300  comprising a home energy management system  310  optionally in communication with remote processing and data store  320  via a network link, e.g., the Internet  330 , and via a remote processing and data store interface  311 . The home energy management system  310  is depicted as configured to monitor one or more energy-consuming devices or appliances  340  via a monitoring interface  312 . The home energy management system  310  is also depicted as configured to control or effect change in one or more energy-consuming devices or appliances  340 , or optionally one or more actuated devices, such as actuated ducts or vents  341 , via a control interface  313 . Via a user interface  350 , the system  300  may display  351  ranked potential, likely, or probable causal situations, PCS(i), and for each PCS(i), the system  300  may display  351  a single or more ranked viable, potentially effective, or likely effective reactions PR(i,j) associated with the PCS(i). The user interface  350  is depicted as configured to receive input  352  from a user indicating a selected reaction, i.e., indicating a selected PR(i, j). The user interface  350  is also depicted as configured to receive input  353  from a user indicating a confirmation of the completion of a previously selected reaction, i.e., input indicating a previously selected PR(i,j) was effected.  FIG. 3  also depicts the home energy management system  310  as comprising a local processing and data store  314 . Via circuitry, a processing unit executing instructions, and/or combinations of both, the local processing  314  may be configured to: (a) monitor the one or more energy-consuming devices; (b) test the measured or estimated energy consumption against one or more thresholds; (c) extract, or request the remote processing and data store  320  extract, potential causal situations, PCS(i), based on the tripped threshold; (d) extract, or request the remote processing and data store  320  extract, potential reactions, PR(i,j), related to the extracted potential causal situations, i.e., related to the PCS(i); (e) apply weights to the potential causal situations, PCS(i), and rank the weighted results; (f) apply weights to the potential reactions, i.e., the PR(i,j), per each PCS(i), and rank the weighted results; (g) output ranked results for display; (h) input user selected PR(i,j); (i) optionally input user confirmation of an action taken relative to a previously selected PR(i,j); and (j) optionally output a control instruction to an energy-consuming device or an actuated device such as a vent or duct. 
     Accordingly, the resident, or user, may be alerted about a detected anomalous condition. For each anomalous condition alert, the resident may be presented with the list of potential situations extracted from the database. The potential situations may be presented in ranked order. For each potential situation presented, the resident may be presented with a list of countermeasures/corrective actions extracted from the database. 
     The resident may select any of the presented situations and any of the presented countermeasures/corrective actions. The selections may be recorded into the database at the home energy management system, into the database of the remote processing, or both databases, and may be used by learning algorithms, e.g., frequency counting and correlating processes. Accordingly, the affects of user selections may be applied in future similar situations and affect the ranked displayed situations and actions. That is, to present the list of possible countermeasures/corrective actions, the system may adjust its correlation of likely causal situations and may adjust its correlation of countermeasures/corrective actions to anomalous conditions for a particular residence, and do so based on the user selections as well as based on general knowledge from preloaded heuristics, and learned information from the community. 
     The system embodiment  300  of  FIG. 3  depicts a control interface in communication with both the energy-consuming devices  340  and actuated ducts and vents  341 . Accordingly, the countermeasures/corrective actions selected by the user that may be activated by the home energy management system  310  directly may be performed automatically. The countermeasures/corrective actions options selected by the resident that require action on the part of the resident, i.e., those outside of HEMS  310  control, e.g., closing a door, are presented with a query to the resident to confirm the action. This confirmation allows the system to record the positive action. In some embodiments, a timeout function may be invoked to presume the resident performed the action, and then an observed result may be used to weight the probability that the resident did indeed perform the action. 
     The system records the change in energy use, if any, presumably due to the countermeasures/corrective actions taken. The recorded results may be used by the learning processing. For example, (a) if the action was effective in restoring the system to a normal condition, i.e., within the expected range of energy usage, then the action may be given higher weighting for future rankings; (b) if the action is increasing in frequency of choice by the user, then the action may be given higher weighting for future rankings; and/or (c) if the action is tied to an infrequently selected possible situation, then the correlation of the anomalous condition to the possible situation, be it residential, appliance, or environmental, may be reassessed. 
     The system processing of the home energy management system may be configured to draw on the historical data for the residence, or that of a similar home, or predictive energy use data from a computer model of the residence, in order to make a prediction of the expected energy use of the entire residence and each smart appliance.  FIG. 4  depicts a functional block diagram of a portion  400  of an exemplary system embodiment. The home energy management system  410  is depicted in  FIG. 4  as comprising a diurnal and seasonal energy usage pattern model  411  that adapts to incorporate information received from one or more instrumented energy-consuming devices  420 , such as watts consumed over a day, and throughout a year, based on a reference clock/calendar  430 . An output of the model  411  is depicted at the expected system home network energy usage  412 , i.e., Exp(EU). The model  411  may also adapt to incorporate information from: (a) a thermometer  440 , e.g., ambient air temperature; (b) a sunlight detector  450 , e.g., one or more photometers positioned to receive solar radiation striking the home or ambient sunlight; and/or (c) a humidity sensor  460 , e.g., a capacitive relative humidity sensor that may be proximate to the ambient air temperature sensor. Additional sensors may be disposed in rooms, halls, closets, and ducts. The home area network may comprise k number of instrumented energy-consuming devices. The HEMS may record the activity of all, i.e., k, instrumented devices and appliances of the home area network. Accordingly, the model  411  processing of  FIG. 4  is also depicted as providing an estimated/expected energy usage of device(k), i.e., the Exp(EU_dev_k)  413 . The data may be cached or stored locally in the HEMS, and then uploaded to remote processing and storage for permanent recording, and for processing that may require higher throughput than the home energy management system and/or for steps that may be executed via non-real time processing. The stored records may include such data as energy usage, time of day, environmental situations, and modes of operation. Static information about the residence, such as configuration, square feet, insulation, and general solar loading, may be recorded in the home energy management system, at the remote processing and storage, or both locations. 
     The processing of the home energy management system may be configured to test for when the actual energy utilization (EU) is outside a normal/expected range around the computed expected/predicted energy use. If outside the range, the home energy management processing and/or remote processing, collectively the processing, may make reference to a database of potential situations that could account for the statistically anomalous observed condition. Each of the potential situations extracted from the database for some embodiments may be ranked by their associated probability or likelihood of being the actual situation in the residence causing the anomalous condition. The ranking may include: (a) the likelihood this situation occurred in similar residences; and (b) the likelihood this situation occurred for this residence, e.g., based on user confirmed historical occurrences. For each possible causal situation extracted from the database, a list of countermeasures/corrective actions, i.e., candidate remedies or potential reactions, may be extracted from the database. Per each possibly likely causal situation, each countermeasure/corrective action associated with the likely causal situation may be ranked by a weighted multi-attribute function comprising attributes such as: (a) effectiveness of restoring the normal state of the residence; (b) the monetary cost or environmental cost of the action; (c) the difficulty/likelihood of the user performing the action; and (d) the previous experience of the user taking or ignoring this particular countermeasure/corrective action. 
       FIG. 5  is a top level flowchart  500  depicting process steps that may be executed by the local processing  314  and/or the remote processing  320  of  FIG. 3 , collectively the processing. For example, the processing may determine the energy usage level (EU) of devices within the array of home networked devices  110  ( FIG. 1 ), and may estimate or project an expected energy usage level (step  510 ), e.g., an Exp(EU). The processing may test whether the determined energy usage is outside of a bound or threshold defined by the expected energy usage (test  520 ), e.g., test whether the present value of the determined, or estimated, energy usage is greater than the expected energy usage, or put another way, is PV(EU)&gt;Exp(EU)? If so, then the processing may extract from a database a set of potential causal situations and the likelihood of each listed causal situation being the actual causal situation (step  530 ), i.e., the PCS(i) and the p(PCS(i)), and their associated potential reactions (step  540 ), i.e., PR(i,j). The processing may draw from a set of weights for the potential causal situations  551 , i.e., w(PCS(i)), applies the weights to the p(PCS(i)), and sorts/ranks the weighted potential causal situations based on the weighted likelihoods, i.e., the weighted p(PCS(i)) (step  550 ). The processing draws from a set of weights for the potential reactions  561 , i.e., w(PR(i,j)), applies the weights to the PR(i,j), and sorts/ranks the weighted potential reactions (step  560 ). The values of the weights may be applied based on the resident&#39;s, or the user&#39;s, profile. The resident profile may be configured by the resident directly or may be learned over time by the system based on the actions selected by the user for similar situations presented previously. So, when a situation in a home occurs that causes a detectable deviation from the normal, expected energy usage, the processing may notify the user, via the user interface, of likely causes and suggested remedies to the deviant energy usage. 
     For example, when the children of a home leave a bedroom window open on a hot day, a processing embodiment may detect the energy usage deviation of kWh/degree of cooling required to cool the home on a hot afternoon. The processing may draw from its database of likely causes for this deviation, and present the list of possible causes and possible actions, e.g., a window is open: shut the window. Other causes and actions in this example may include; (a) an air filter is choked: (i) clean the air filter or (ii) replace the air filter; (b) a door is open: close the door; and (c) the A/C coolant is low: (i) recharge A/C system with coolant or (ii) repair coolant line. 
       FIG. 6  is a functional block diagram depicting another exemplary system embodiment  600  comprising a home energy management system  310  optionally engaging one or more energy-producing devices  640  such as: one or more residential solar arrays, wind turbines, and/or optionally engaging one or more energy storing devices  641 , such as a chemical battery. 
     The system processing of the home energy management system may be configured to draw on the historical data for the residence, or that of a similar home, or predictive energy generation and/or storage data from a computer model of the residence, in order to make a prediction of the expected energy generation and/or storage of the entire residence and each smart appliance. 
       FIG. 7  depicts a functional block diagram of a portion  700  of an exemplary system embodiment. The home energy management system  710  is depicted as comprising a diurnal and seasonal energy generation and/or energy storage pattern model  711  that adapts to incorporate information received from one or more instrumented energy-generating and/or energy-storing devices  720 , such as watts generated over a day, and throughout a year, based on a reference clock/calendar  730 . An output of the model  711  is depicted at the expected system home network energy generation  712 , i.e., Exp(EG). The model  711  may also adapt to incorporate information from: (a) a thermometer  740 , e.g., ambient air temperature; (b) a sunlight detector  750 , e.g., one or more photometers positioned to receive solar radiation striking the home or ambient sunlight; and/or (c) a humidity sensor  760 , e.g., a capacitive relative humidity sensor that may be proximate to the ambient air temperature sensor. Additional sensors may be disposed in rooms, halls, closets, and ducts. The home area network may comprise k number of instrumented energy-consuming devices. The HEMS may record the activity of all, i.e., k, instrumented devices and appliances of the home area network. Accordingly, the model  711  processing of  FIG. 7  is also depicted as providing an estimated/expected energy usage of device(k), i.e., the Exp(EU_dev_k)  713 . The data may be cached or stored locally in the HEMS, and uploaded to remote processing and storage for permanent recording, and for processing that may require higher throughput than the home energy management system and/or for steps that may be executed via non-real time processing. The stored records may include such data as energy usage, time of day, environmental situations, and modes of operation. Static information about the residence, such as configuration, square feet, insulation, and general solar loading, may be recorded in the home energy management system, at the remote processing and storage, or both locations. 
       FIG. 8  is a top level flowchart  800  depicting process steps that may be executed by the local processing  314  and/or the remote processing  320  of  FIG. 6 , collectively the processing. For example, the processing may determine the energy generation level (EG) of energy-producing devices  640  ( FIG. 6 ), and may estimate or project an expected energy production level (step  810 ), e.g., an Exp(EG). The processing may test whether the determined energy production, and/or storage, is outside of a bound or threshold defined by the expected energy generation (test  820 ), e.g., test whether the present value of the determined, or estimated, energy generation and/or energy storage is less than the expected energy production and/or storage, or put another way, is PV(EG)&lt;Exp(EG)? If so, then the processing extracts from a database a set of potential causal situations and the likelihood of each listed causal situation being the actual causal situation (step  830 ), i.e., the PCS(i) and the p(PCS(i)), and their associated potential reactions (step  840 ), i.e., PR(i,j). The processing may draw from a set of weights for the potential causal situations  851 , i.e., w(PCS(i)), applies the weights to the p(PCS(i)), and sorts/ranks the weighted potential causal situations based on the weighted likelihoods, i.e., the weighted p(PCS(i)) (step  850 ). The processing may draw from a set of weights for the potential reactions  861 , i.e., w(PR(i,j)), applies the weights to the PR(i,j), and sorts/ranks the weighted potential reactions (step  860 ). The values of the weights may be applied based on the resident&#39;s, or the user&#39;s profile. The resident profile may be configured by the resident directly or may be learned over time by the system based on the actions selected by the user for similar situations presented previously. So, when a situation in a home occurs that causes a detectable deviation from the normal, expected energy production/generation and/or energy storage, the processing can notify the user, via the user interface, of likely causes and suggested remedies to the deviant energy production/generation. 
     It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further it is intended that the scope of the present invention is herein disclosed by way of examples should not be limited by the particular disclosed embodiments described above.