Systems and methods for managing energy consumption

Abstract

Systems and methods for tracking, analyzing, modelling and optimizing energy usage, energy costs and greenhouse gas (GHG) emissions for an energy-consuming entity having energy- consuming assets are described. The systems and methods involve obtaining energy consumption data for energy types from a plurality of energy sources for a time period, analyzing the energy consumption data for each energy type for the time period, including converting energy consumption to a common unit, determining energy cost, and determining emissions, and providing a user with the analysis of the energy consumption, cost and emissions for each energy type and a total for all types. Modelling is used to predict how a change in an energy-consuming asset, energy source and / or energy type will affect energy usage, energy costs and emissions. Energy use may be optimized by determining the optimal operation of one or more of an entity's energy-consuming assets.

Description

Systems and Methods for Managing Energy ConsumptionTECHNICAL FIELD

[0001] The disclosure relates to energy usage, and more specifically to systems and methods for tracking, analyzing and optimizing energy usage, energy costs and greenhouse gas (GHG) emissions.BACKGROUND

[0002] Energy usage for a consumer is a relatively complex field. Consumers constantly purchase or generate energy to power various energy-consuming assets, but they have a limited understanding of their energy usage, costs, GHG emissions and choices around energy. This is partly because energy is purchased from a variety of sources, for example from utility companies, fuel companies, or other companies, and / or may be generated directly by a consumer, for example through solar panels. The complexity of energy usage is due to the myriad of forms and units that energy comes in, including electricity from renewable and non-renewable sources, natural gas, gasoline fuel, diesel fuel, propane, bio-fuels, jet fuels, and more. Consumers often want to better manage their energy consumption, but they don’t know how. There is a lack of support and readily available data for consumers looking to improve their understanding of energy consumption or make decisions around energy consumption.SUMMARY

[0003] In accordance with the present disclosure, there is provided a network-based energy consumption tracking method for an entity having a plurality of energyconsuming assets comprising the steps: obtaining energy consumption data for a plurality of energy types from a plurality of energy sources for a time period; analyzing the energy consumption data for each energy type for the time period, including: converting energy consumption to a common unit; determining energy cost; and determining emissions associated with the energy consumption; and providing a user with the analysis of the energy consumption, energy cost and emissions for each energy type and a total for all types.

[0004] In an aspect of the disclosure, there is provided a network-based energy consumption tracking system for an entity having a plurality of energy-consumingassets, the system comprising: a processor accessible on a computer network; a memory for storing instructions, that when executed by the processor, is configured for: obtaining energy consumption data for a plurality of energy types from a plurality of energy sources for a time period; analyzing the energy consumption data for each energy type for the time period, including: converting energy consumption to a common unit; determining energy cost; and determining emissions associated with the energy consumption; and providing a user with the analysis of the energy consumption, energy cost and emissions for each energy type and a total for all types.

[0005] Determining the energy cost may include determining the cost for each of the energy, taxes and fees.

[0006] The energy consumption data may be obtained by any one of or combination of: user input, directly from the energy source, extraction from a bill or receipt, and obtained from a sensor connected to one or more of the energy-consuming assets.

[0007] The method or system may further comprise calculating an estimated change in the energy consumption, energy cost and / or emissions due to switching, eliminating, or adding an energy-consuming asset. The method or system may further comprise calculating an estimated change in the energy consumption, energy cost and / or emissions due to a change in one of more of the energy sources or energy types. The method or system may further comprise acquiring electrical grid data including the energy types supplying an electrical grid for the time period, and determining the emissions for electricity from the electrical grid based on the electrical grid data.

[0008] The method or system may further comprise determining optimal use in terms of energy consumption, energy cost and / or emissions for one of the energyconsuming assets alone or in combination with one or more of the other energyconsuming assets. Optimal use may comprise when to switch between using different energy-consuming assets. Optimal use may be determined based on acquired data related to one or more of: weather, location, time of day, energy costs, energy demand, and energy availability. The method may further comprise automatically controlling one or more of the energy-consuming assets in line with the optimal use.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In the Detailed Description section below, one or more embodiments of the present technology are described in relation to the attached figures. These embodiments are intended to provide a better understanding of the invention, how the invention may be put into practice, and to demonstrate some of the advantages of the invention. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of various embodiments of the invention. Similar reference numerals indicate similar components.FIG. 1 is a schematic diagram of a network in accordance with aspects of the disclosure.FIG. 2 is a diagram of an energy consumption server in accordance with aspects of the disclosure.FIG. 3 is a flowchart of steps performed by the energy consumption server in accordance with aspects of the disclosure.DETAILED DESCRIPTION

[0010] The present disclosure relates to systems and methods for tracking, analyzing and optimizing energy usage, energy costs and greenhouse gas (GHG) emissions for an energy-consuming entity. An energy-consuming entity refers to any individual, group, organization, industry and / or structure that consumes energy, which can be a household, business, government, NGO, charity, and more. An energy-consuming entity typically has a multitude of energy-consuming assets, including but not limited to vehicles, such as internal combustion engine (ICE) vehicles, electric vehicles (EVs), recreational vehicles (RVs), planes, boats and motorbikes; temperaturecontrol devices such as furnaces, heat pumps, air conditioners, fireplaces and water heaters; power-equipment such as lawnmowers and generators; home appliances such as fridges, freezers, washers and dryers; industrial or commercial equipment, and more.

[0011] Energy-consuming assets may utilize a wide range of energy types, both renewable and non-renewable. Examples include but are not limited to solar, wind, geothermal, tidal, hydro, natural gas, coal, nuclear, gasoline, diesel fuel, thermal heat, oil, wood, propane, and more. Energy may be obtained by an energyconsuming entity from a supplier, such as a utility company, fuel company, gas station, or other company or organization. Energy may also be generated directly, forexample through solar panels, or generated by an energy-consuming entity, for example a heat pump. An energy-consuming entity may also sell the energy it generates. The units energy is measured in can vary widely. For example, electricity may be in kilowatt hours (kWh) or joules (J), natural gas in gigajoules (GJ), Therms, cubic metres (m3), cubic feet (cf) or British thermal units (Btu), gasoline and diesel fuel in liters (L) or gallons (gal), propane in pounds (lbs) or kilograms (kg), and jet fuel in tonnes (t), kilograms (kg), tons, or pounds (lbs).

[0012] Energy purchases often include various fees and taxes, which may be obvious or obscured, including various levels of taxes (provincial / state, municipal, federal) and various types of taxes (sales tax, carbon tax, fuel tax, excise tax). Energy purchases may include a fixed delivery fee provided to a utility company or other supplier, plus a variable fee based on the amount of energy purchased.

[0013] Based on the complexities around energy described above, it is difficult for an energy-consuming entity to understand their energy usage, and understand how making a change to their behaviors, energy-consuming assets, energy types used, and / or energy sources will affect their total energy consumption, emissions and costs. For example, an entity may want to know the result of replacing a gasoline-powered vehicle with an electric vehicle. A blanket statement regarding a change in energy usage due to this replacement is often inaccurate, as it is highly dependent on individual factors like geographic location, weather, vehicle usage and driving patterns, available electricity sources and costs, gasoline sources and costs, fees, taxes, and more. By obtaining and processing an entity’s energy consumption, and combining it with other data, the subject systems and methods are able to analyze and optimize an entity’s energy usage, while also modelling different scenarios to help a user make informed decisions around energy.

[0014] Various aspects of the disclosure will now be described with reference to the figures. For the purposes of illustration, components depicted in the figures are not necessarily drawn to scale. Instead, emphasis is placed on highlighting the various contributions of the components to the functionality of various aspects of the disclosure. A number of possible alternative features are introduced during the course of this description. It is to be understood that, according to the knowledge and judgment of persons skilled in the art, such alternative features may be substituted in various combinations to arrive at different embodiments of the present disclosure.

[0015] FIG. 1 depicts an example of a network 10 with various components within the network communicatively coupled via a communication medium 12, such as the internet. An energy consumption server 20 comprising a processor and a non- transitory memory communicates with a user computing device 50 to transmit and receive energy consumption information and / or instructions. User computing devices may be any type of computing device, e.g. a phone, tablet or computer, comprising a processor and a non-transitory memory. While only one user computing device is shown, there may be thousands or millions of user computing devices. Likewise, while only one energy consumption server 20 is shown, the functions performed by the energy consumption server may be performed by numerous servers and / or a cloud computing platform.

[0016] The user computing device 50 and / or the energy consumption server 20 may communicate and / or coordinate with one or more: energy-consuming assets 40a, b; utility providers 60; weather servers 62; energy markets 64; government information 66; equipment specifications 68; and other third-party data 70. Communication between the energy consumption server 20 and other components on the network may happen at predetermined time intervals or in real-time or near real-time.

[0017] FIG. 2 illustrates an example of an energy consumption server 20. The energy consumption server 20 may comprise one or more of a data acquisition module 22, a data processing module 24, a data analysis module 26, a modelling module 28, an optimization module 30 and a database 32. The various modules perform different steps in the energy consumption tracking method as further detailed below. The database 32 stores relevant data, including acquired data, analyzed data, processed data, and modelling data. Those skilled in the art would understand that the functionality performed by each module may be performed by other modules, and that the various elements, databases and modules may be organized in different ways or may be external to the energy consumption server.

[0018] FIG. 3 illustrates an example of steps that may be performed by an energy consumption server for energy tracking, processing, analyzing, optimization and modelling. The specific steps and modules of the energy consumption server are described in more detail below with reference to FIGS. 1-3.

[0019] Energy-consuming assets 40a, b may obtain information about the energy usage of the asset and / or optimize energy usage of the asset on its own or in conjunction with other energy-consuming assets. To allow for this communicationand / or coordination, the energy-consuming assets may include sensors or other devices that obtain and transmit data between the energy-consuming assets and other components on the network. The sensors may be “smart” sensors which have computing capabilities and can receive data and / or instructions from the energy consumption server, the user computing device, and / or elsewhere. Examples of sensors include but are not limited to smart thermostats, smart meters, smart inverters, smart chargers such as EV chargers, smart appliances (e.g. fridges, washers, dryers), amp meters, smart vehicles, and more.Data Acquisition Module 22

[0020] The data acquisition module 22 obtains data regarding energy consumption from one or more sources for an entity’s energy-consuming assets 40a, b. The energy consumption data may include the type of energy consumed, the amount of energy consumed, the period of time in which the energy is consumed, and the cost of the energy, including any fees and taxes. The data may be obtained through manual, automated or semi-automated input. It may be obtained for a specific period of time, e.g. for a day, week, month, etc., or it may be obtained in real-time or near real-time. If available, the energy consumption data may include data on GHG emissions.

[0021] Energy consumption data from different sources may be obtained in different manners. For example, energy consumption data may be obtained from user input, directly from a utility provider or another provider, extracted from a utility bill or sales invoice, and / or obtained from a sensor connected to an energy-consuming asset.

[0022] As an example, when a user purchases fuel from a gas station to fuel an energy-consuming asset like a gasoline- or diesel-powered vehicle, they may input the energy consumption information into a user computing device based on the information provided at the fuel pump or on the fuel receipt. They may enter the type of fuel (e.g. regular, midgrade or premium gasoline, diesel), the cost of the fuel, the amount of fuel purchased in selected units (e.g. gallons, liters, pounds), and indicate what energy-consuming asset(s) the fuel purchase is for. Alternatively, the user may upload a fuel purchase receipt through a user computing device, and the energy consumption data may be automatically extracted from the fuel receipt by the data acquisition module.

[0023] In another example, a user uploads a utility bill through a user computing device for the purchase of electricity or natural gas for their home, and the energy consumption data is extracted from the utility bill.

[0024] The data acquisition module may obtain data from one or more weather servers 62 to obtain detailed weather information for a location, which may include historical, current and predicted future weather information such as temperature, wind speed, wind direction, humidity, solar radiation, and cloud cover.

[0025] The data acquisition module may obtain data from one or more energy markets 64. Energy market data may include historical, current and predicted future market data, such as energy prices and energy sources.

[0026] The data acquisition module may obtain data from one or more government information 66 servers / databases to obtain data on government fees and taxes for various forms of energy in different jurisdictions, which may include sales tax, carbon tax, fuel tax, excise tax, and other taxes and fees.

[0027] The data acquisition module may obtain data from one or more equipment specification 68 servers / databases to obtain specification data for energy-consuming assets. Data for each asset may include the model, year of manufacture, year of purchase, efficiency, geographic location, operational specifications, and other relevant information.

[0028] The data acquisition module may obtain data from one or more third party 70 servers / databases to obtain data and / or analyses relevant to an entity’s energy usage. For example, this may include third-party generated thermal images of a building or structure that is part of an entity’s energy-consuming assets. Another example is efficiency data on energy-consuming assets, such as EVs.Data Processing Module 24

[0029] The data processing module 24 processes the data obtained by the data acquisition module 22. As described above, energy is measured in many different units. One processing step is to convert energy usage from each source to a common unit, for example a GJ or BTU. Generally, this is done by multiplying a measurement unit by a factor to obtain the desired common unit.

[0030] Another processing step is determining the cost of energy usage per common unit from each source and determining the fees and the taxes for energy usage from each source. The data processing module utilizes the data acquired regarding fees and taxes for different types of energy in various jurisdictions. Based on the amount of energy consumed and the cost of the energy, the data processing module determines what fees and taxes were paid as part of the energy purchase. Forexample, knowing that a given volume of gasoline was purchased in a specific city, the data processing module determines the sales tax, carbon tax, fuel tax, excise tax, and any other fees and taxes that are required for gasoline sales in that jurisdiction, and then determines what the cost was for the fuel, taxes and fees.

[0031] Another processing step is determining GHG emissions associated with the energy usage. The majority of GHG emissions are carbon dioxide (CO2), however there may be smaller amounts of methane (CH4) and nitrous oxide (N2O) emitted. The data processing module may determine the Scope 1 , or direct emissions, associated with an entity’s energy usage. Scope 2 emissions may also be determined, which include indirect emissions from the entity’s energy production and consumption. GHG emissions may be determined in tCO2e, which is tonnes of carbon dioxide equivalent and is a measure of how much a gas contributes to global warming, relative to carbon dioxide. Alternatively, GHG emissions may be determined in different units that align with commonly used units in a jurisdiction.

[0032] The manner of determining GHG emissions for each energy source varies based on the energy source. The data processing module may use data from the data acquisition module to calculate or estimate GHG emissions for the amount of energy consumed in a given time period, based on the source of the energy, which may be from renewable or non-renewable sources. For example, for emissions associated with electricity from an electric grid, data may include what energy sources (e.g. coal, natural gas, wind, solar, etc.) were generating energy for the electric grid at a given time and in what proportions, and the GHG emissions associated with each energy source. From this data, the data processing module can determine the emissions associated with an amount of electricity consumption at a given time.

[0033] In another example, the data processing module may use data from the data acquisition model regarding commonly accepted GHG emissions associated with various types of fuel, for example gasoline, diesel fuel, ethanol, natural gas, etc. Fuels may be blended to include mixtures of fuel types that have different GHG emissions associated with them. For example, gasoline is commonly blended with ethanol, and natural gas may be blended with hydrogen and / or other fuels, like biofuels or ethanol, all of which affect GHG emissions. Data on the composition of fuels may be obtained by the data acquisition module from one or more sources, for example from the energy provider, if available. If not, estimates of fuel compositionmay be calculated based on the best available data for a period of time in question when the fuel was consumed. The data processing module uses this data to calculate GHG emissions associated with consumption of a quantity of fuel having a particular composition.Data Analysis Module 26

[0034] The data analysis module 26 uses the processed data from the data processing module 24 to provide a data analysis to the user through the user’s computing device. The data analysis may provide energy consumption information, including total energy consumption for a given period of time (e.g. monthly, annually), energy consumption per energy source (e.g. natural gas, electricity, fuel), energy consumption per energy-consuming asset (e.g. fireplace, furnace, a specific vehicle, appliances, lighting), and comparisons of energy consumption changes over time, per energy source and / or per energy-consuming asset. The data analysis may provide energy cost information, including the total cost of energy consumption for a given period of time, the total cost of energy consumption per source, the total cost of energy-consuming asset, the total cost of each type of tax and fee, the total cost of each type of tax and fee per energy source, and comparisons of energy cost changes over time, per energy source and / or per energy-consuming asset.Modelling Module 28

[0035] The modelling module 28 provides data modelling to understand how a change is expected to affect an entity’s energy usage, including consumption, cost and / or emissions. The change may be to an entity’s energy-consuming assets, energy sources, and / or energy costs. This modelling is done through various analytical techniques including the use of regression analysis, statistics, artificial Intelligence, etc. Change decisions have an inherent uncertainty built into them, since an expectation of the future is built into the decision process. The modelling may calculate different scenarios and likely outcomes to address the uncertainty. Typically, various inputs are used in the modelling based on possible operational decisions, which include assumptions about how energy-consuming assets will be operated.

[0036] Examples of data modelling include:- The change in energy consumption, costs and emissions associated with replacing an internal combustion engine (ICE) vehicle with an electric vehicle (EV), i.e. replacing an energy-consuming asset.- The change in energy costs and emissions associated with buying electricity from a renewable energy provider instead of a non-renewable energy provider, i.e. changing a source of energy.- The change in energy costs associated with switching to a new energy provider having an energy contract at a different rate.- The change in energy consumption, costs and emissions associated with replacing a furnace with a new furnace and air conditioner, compared to replacing a furnace with a new furnace and heat pump i.e. changing energy-consuming assets.- The change in energy consumption, costs and emissions associated with installing solar panels, i.e. adding a new energy-consuming asset.- The change in energy costs associated with a change in taxes, for example a change in carbon tax.- The change in energy consumption, costs and emissions associated with making changes to a building, for example replacing windows, adding insulation.

[0037] The steps for modelling are scenario-dependent. For example, one modelling scenario is what happens when an entity replaces an ICE vehicle with an EV. The modelling module may use historical energy consumption data for the ICE vehicle to predict future energy consumption of a vehicle, whether it is the current ICE vehicle or a replacement EV. Based on a number of parameters, such as vehicle model, year, efficiency, weather in the location it is typically driven, etc., it is determined how much electricity would be needed to use the EV in the same manner as the current ICE vehicle. Given the user’s electricity utility provider, location and rate, the electricity consumption, cost and emissions for the EV is predicted and compared to the ICE vehicle fuel consumption, cost and emissions.

[0038] In another example, a scenario is modelled for installing solar panels on a building. The modelling module considers the cost of the solar panels, installation costs, and the expected power generation given the historical weather patterns for the location of the solar panels. Then, considering the user’s current electrical usage,cost and emissions associated with the user’s utility company, and how this will change with the electricity generated from the solar panels, modelling data is provided to inform a user how their annual electricity usage, costs and emissions will change, and how long it will take to recoup the cost of the solar panels.Optimization Module 30

[0039] The optimization module 30 makes decisions on how to best optimize an entity’s energy usage in terms of consumption, cost and / or emissions for the entity’s current energy-consuming assets. A user may be able to prioritize different aspects of energy usage, for example they may wish to optimize cost over emissions, or vice versa. The optimization may be in relation to optimizing use of one energy-consuming asset or coordinating optimization of multiple energy-consuming assets. The optimization decision is determined through various analytical techniques including the use of regression analysis, statistics, artificial Intelligence, etc. Some examples of optimization decisions are as follows.

[0040] An entity may have multiple energy-consuming assets used for temperature control in a building, such as a heat pump, furnace, air conditioner, fireplace, electric baseboards, in floor heating, and more. An optimization decision would be determining which energy-consuming asset or combination of assets should be used to heat or cool a building at any given moment.

[0041] An entity may have multiple vehicles, such as an ICE vehicle, an EV and a motorcycle. An optimization decision would be determining which vehicle to use for a specific trip.

[0042] An entity may have multiple sources of electricity, for example a renewable source like solar panels and a non-renewable source through the electricity grid. An optimization decision would be determining which source to draw on at any given moment for a given purpose.

[0043] An entity has many electricity-consuming assets, and some of these assets consume large amounts of electricity when in use, for example charging an EV and operating a large appliance like a washer, dryer or dishwasher. An optimization decision would be coordinating the optimal timing of electricity consumption for each asset to ensure enough electricity is available, and to optimize electricity use in terms of cost and / or emissions.

[0044] The factors taken into consideration when making an optimization decision are dependent on the decision being made. Factors that may affect the decision include but are not limited to the time of day, the current and expected weather conditions, the amount of fuel or charge available in an asset, the current price of energy, the available energy sources, the efficiency of the assets under various weather conditions, and more.

[0045] As a more specific example of energy optimization, a first energy-consuming asset may be an EV with a smart EV charger, and a second energy-consuming asset may be a smart washing machine, both located in the same house. The first and second energy-consuming assets may provide energy consumption data to a user’s computing device which is communicated to the energy consumption server. The energy consumption data may be that both the smart EV charger and the smart washing machine are in use or are desired to be in use. The energy consumption server processes the information and determines both the EV charger and the smart washing machine should not operate simultaneously due to electrical amp limitations, and communicates this through the user’s computing device. The energy consumption server may automatically control the smart EV charger to stop charging the EV until the washing machine is no longer in operation, or it may provide a notification to the user who can manually decide to pause operation of one of the energy-consuming assets until the other energy-consuming asset is no longer in operation.

[0046] In another example, an entity has an electric-powered heat pump and a natural gas furnace for heating a building. The optimization module accesses various data, which may include current temperature, heat pump efficiency, furnace efficiency, current cost of electricity, current cost of natural gas, emissions for electricity based on the current sources supplying the electrical grid, emissions for natural gas based on natural gas composition, and more. The optimization module uses this data to determine the cost and emissions for using the heat pump versus using the furnace and decides which asset is optimal to use at the moment. The decision may be communicated to a user and / or the operation of the furnace and heat pump may be automatically controlled via the network.

[0047] In another example, an entity has solar panels on a building and is also connected to the electrical grid. The entity’s contract with an electricity provider allows them to either sell electricity they generate from their solar panels to theelectrical grid at a certain rate, which is beneficial in high-producing periods (i.e. summer) or buy electricity from the electrical grid at another rate, which is beneficial in low-producing periods (i.e. winter). The entity cannot continually switch back and forth between buying or selling, therefore they need to determine a date to switch between contracts, which is usually done twice a year. The optimization module can use various data to determine the optimal switch dates or a range of dates to optimize costs. Data may include solar panel specifications (e.g. area of panels, tilt, azimuth, direction, location, inverter), historical solar electricity production, historical electricity consumption, historical weather, and more.

[0048] Although the present disclosure has been described and illustrated with respect to preferred embodiments and preferred uses thereof, it is not to be so limited since modifications and changes can be made therein which are within the full, intended scope of the disclosure as understood by those skilled in the art.

Claims

CLAIMS1 . A network-based energy consumption tracking method for an entity having a plurality of energy-consuming assets comprising the steps: obtaining energy consumption data for a plurality of energy types from a plurality of energy sources for a time period; analyzing the energy consumption data for each energy type for the time period, including: converting energy consumption to a common unit; determining energy cost; and determining emissions associated with the energy consumption; and providing a user with the analysis of the energy consumption, energy cost and emissions for each energy type and a total for all types.

2. The method of claim 1 , wherein determining the energy cost includes determining the cost for each of the energy, taxes and fees.

3. The method of claim 1 or 2, wherein the energy consumption data is obtained by any one of or combination of: user input, directly from the energy source, extraction from a bill or receipt, and obtained from a sensor connected to one or more of the energy-consuming assets.

4. The method of any one of claims 1 -3, further comprising calculating an estimated change in the energy consumption, energy cost and / or emissions due to switching, eliminating, or adding an energy-consuming asset.

5. The method of any one of claims 1 -4, further comprising calculating an estimated change in the energy consumption, energy cost and / or emissions due to a change in one of more of the energy sources or energy types.

6. The method of any one of claims 1 -5, further comprising acquiring electrical grid data including the energy types supplying an electrical grid for the time period, and determining the emissions for electricity from the electrical grid based on the electrical grid data.

7. The method of any one of claims 1-6, further comprising determining optimal use in terms of energy consumption, energy cost and / or emissions for one of theenergy-consuming assets alone or in combination with one or more of the other energy-consuming assets.

8. The method of claim 7, wherein the optimal use comprises when to switch between using different energy-consuming assets.

9. The method of claim 7 or 8, where the optimal use is determined based on acquired data related to one or more of: weather, location, time of day, energy costs, energy demand, and energy availability.

10. The method of any one of claims 7-9, further comprising automatically controlling one or more of the energy-consuming assets in line with the optimal use.

11. A network-based energy consumption tracking system for an entity having a plurality of energy-consuming assets, the system comprising: a processor accessible on a computer network; a memory for storing instructions, that when executed by the processor, is configured for: obtaining energy consumption data for a plurality of energy types from a plurality of energy sources for a time period; analyzing the energy consumption data for each energy type for the time period, including: converting energy consumption to a common unit; determining energy cost; and determining emissions associated with the energy consumption; and providing a user with the analysis of the energy consumption, energy cost and emissions for each energy type and a total for all types.

12. The system of claim 11 , wherein determining the energy cost includes determining the cost for each of the energy, taxes and fees.

13. The system of claim 11 or 12, wherein the energy consumption data is obtained by any one of or combination of: user input, directly from the energy source, extraction from a bill or receipt, and obtained from a sensor connected to one or more of the energy-consuming assets.

14. The system of any one of claims 11-13, further comprising calculating an estimated change in the energy consumption, energy cost and / or emissions due to switching, eliminating, or adding an energy-consuming asset.

15. The system of any one of claims 11-14, further comprising calculating an estimated change in the energy consumption, energy cost and / or emissions due to a change in one of more of the energy sources or energy types.

16. The system of any one of claims 11-15, further comprising acquiring electrical grid data including the energy types supplying an electrical grid for the time period, and determining the emissions for electricity from the electrical grid based on the electrical grid data.

17. The system of any one of claims 11-16, further comprising determining optimal use in terms of energy consumption, energy cost and / or emissions for one of the energy-consuming assets alone or in combination with one or more of the other energy-consuming assets.

18. The system of claim 17, wherein the optimal use comprises when to switch between using different energy-consuming assets.

19. The system of claim 17 or 18, where the optimal use is determined based on acquired data related to one or more of: weather, location, time of day, energy costs, energy demand, and energy availability.

20. The system of any one of claims 17-19, further comprising automatically controlling one or more of the energy-consuming assets in line with the optimal use.

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