OPTIMIZING HOME ENERGY EFFICIENCY AND DEVICE UPGRADE SCHEDULING

A method, a computer system, and a computer program product for optimizing home energy efficiency and device upgrade scheduling is provided. The present invention may include gathering data about home utilities and energy consumption patterns over time. The present invention may include training a machine learning model based on the gathered data. The present invention may include detecting inefficient devices or utility configurations based on the trained machine learning model. The present invention may include gathering data about current and future advances in home device and energy technology. The present invention may include determining optimal utility configurations and detecting candidate device upgrades. The present invention may include performing cost/benefit analysis based on the determined utility configurations and device upgrades. The present invention may include providing personalized recommendations to the user based on the cost/benefit analysis.

BACKGROUND

The present invention relates generally to the field of machine learning, and more particularly to optimizing home energy efficiency.

Home energy consumption and costs are determined by device usage patterns and utility costs. Home devices including, but not limited to, televisions, refrigerators, washers, dryers, ovens, stoves, water heaters, and electric vehicles, have differing levels of energy consumption and utility requirements. Homes may have one or more utilities available to deliver energy to these devices, including, but not limited to, electricity, gas, and solar power. Upgrading an appliance/device involves startup costs such as installation fees and rerouting or modification of utility lines connected to the home.

As technology for home devices advances, new models become available that may offer greater energy efficiency than existing models. New models may also require specific utility configurations and infrastructure that may or may not be available at all geographic locations. Decisions about whether or not to upgrade a device must balance utility availability and installation costs with the long-term benefits of improved energy efficiency.

SUMMARY

Embodiments of the present invention disclose a method, a computer system, and a computer program product for optimizing home energy efficiency and device upgrade scheduling. The present invention may include gathering data about home utilities and energy consumption patterns over time. The present invention may include training a machine learning model based on the gathered data. The present invention may include detecting inefficient devices based on the trained machine learning model. The present invention may include gathering data about current and future advances in home device and energy technology. The present invention may include determining optimal utility configurations and detecting candidate device upgrades. The present invention may include performing cost/benefit analysis based on the determined device upgrades. The present invention may include providing specialized recommendations to the user based on the cost/benefit analysis.

DETAILED DESCRIPTION

The following exemplary embodiments provide a system, method and computer program product for optimizing home energy efficiency and device upgrade scheduling. As such, the present embodiment has the capacity to improve the technical field of machine learning optimization systems by analyzing home energy consumption patterns, determining an optimal utility and device configuration, and recommending upgrades to the user. More specifically, the present invention may include gathering data about home utilities and energy consumption patterns over time. The present invention may include gathering data about current and future advances in home device and energy technology. The present invention may include training a machine learning model based on the gathered data. The present invention may include detecting inefficient devices based on the trained machine learning model. The present invention may include determining optimal utility configurations and detecting candidate appliance upgrades. The present invention may include performing cost/benefit analysis based on the determined device upgrades. The present invention may include providing specialized recommendations to the user based on the cost/benefit analysis.

As previously described, home energy consumption and costs are determined by device usage patterns and utility costs. Home devices including, but not limited to, televisions, refrigerators, washers, dryers, ovens, stoves, water heaters, and electric vehicles, have differing levels of energy consumption and utility requirements. Homes may have one or more utilities available to deliver energy to these devices, including, but not limited to, electricity, gas, and solar power. Upgrading a device involves startup costs such as installation fees and rerouting or modification of utility lines connected to the home.

As technology for home devices advances, new models become available that may offer greater energy efficiency than existing models. New models may also require specific utility configurations and infrastructure that may or may not be available at all geographic locations.

There may be particular instances when a home device must be replaced; for example, due to malfunction of the existing device, or degradation of the device's energy efficiency over time, leading to increased utility costs. However, the decisions about which devices to upgrade, which newer models to choose, and when to perform the upgrades require complex cost/benefit analysis. These decisions are often made with incomplete information. This may lead to excessive costs and reduced benefits obtained by upgrading to suboptimal models or upgrading at a suboptimal time. At worst, large costs may be sunk into upgrades that are incompatible with the house's current utility configuration, necessitating further costly infrastructure upgrades or wasted effort. It is difficult for consumers who are not experts to make informed decisions without detailed knowledge of the valid options that are available and prospective future technologies that may provide increased benefit over time.

Therefore, it may be advantageous to, among other things, analyze energy consumption patterns of the home, detect high-cost or inefficient devices, determine optimal replacement models for those devices that are compatible with the house's utility configuration, and determine an optimal timeline for upgrading the devices based on cost/benefit analysis.

According to at least one embodiment, the present invention may improve a home's energy efficiency by determining an optimal schedule for home device upgrades, given the home's energy consumption patterns and available utilities.

According to at least one embodiment, the present invention may gather data about a home's energy consumption. Gathered data may include, but are not limited to, utility usage per hour, weather conditions, time of day, date, and geographical location.

According to at least one embodiment, the present invention may include a trained machine learning model which utilizes the gathered data to detect high cost and inefficient devices.

According to at least one embodiment, the present invention may include gathering data about current and future advances in home device and energy technology compatible with the house's utility configuration. The gathered data may also include utility upgrades to expand the list of available device models.

According to at least one embodiment, the present invention may perform an analysis to determine optimal device upgrades and timing to minimize cost and maximize the energy efficiency of the home. The analysis may be used to provide recommendations to the user.

The recommendations and instructions for device upgrades may be provided on a user dashboard. The user dashboard may show the inefficient devices, recommended new models, and expected installation costs and future savings obtained by performing the recommended upgrades.

Referring toFIG. 1, an exemplary energy optimization system100in accordance with one embodiment is depicted.FIG. 1provides only an illustration of one embodiment of the present invention and does not imply any limitations with regard to the environments in which different embodiments may be implemented. In the depicted embodiment, energy optimization system100may include an energy fundamental analysis module110configured to receive regional utility data from database105. The energy optimization system100may also include an energy consumption prediction module120configured to receive energy consumption data from database115and output data from energy fundamental analysis module110. The energy optimization system100may also include a personalized energy management module130configured to receive device data from database125and output data from energy consumption prediction module120. The output from the personalized energy management module130may be sent to display140. The output from the personalized energy management module130shown on display140may include recommendations and timelines for home device upgrades, utility modifications, and projected costs and savings resulting from those recommendations.

Regional utility data105may include data relevant to the specific location of the target home. This may include information on which utilities are available at the location, weather patterns throughout the year, costs of the available utilities, tax considerations, and risk of natural disasters, among other things. The data105may be obtained from online sources using a machine learning algorithm. This machine learning algorithm may include a natural language processing algorithm. The data105may be used by energy fundamental analysis module110to train a machine learning model to predict energy consumption and cost trends throughout the year at the specified location. The machine learning model may be a time series deep learning model or a reinforcement learning model.

The time series deep learning model or reinforcement learning model may learn and predict the expected energy consumption of a home at the specified location depending on the time of day and day of the year.

The time series deep learning model or reinforcement learning model may learn and predict the expected energy costs per utility of a home at the specified location depending on the time of day and day of the year.

Energy consumption data115may include data specific to the target home. This may include hourly utility consumption rates and manufacturer and model information for home devices that consume significant amounts of energy. This may include, but is not limited to, heating systems, water heaters, stoves, ovens, refrigerators, televisions, computers, and electric vehicles. Hourly or 15-minute utility usage rate data may be obtained from the utility providers. This data may be used together with the output from energy fundamental analysis module110to train a machine learning model to predict hourly energy consumption and costs per home device at the home. The machine learning model may be a time series deep learning model or a reinforcement learning model.

Device data125may include a list of the costs, energy consumption rates, and utility requirements of device models that could replace the devices contained in data115. Data125may be obtained from online sources using a machine learning algorithm. The machine learning algorithm may include a natural language processing algorithm.

Referring now toFIG. 2, an operational flowchart illustrating the exemplary energy fundamental analysis module110used by the energy optimization system100according to at least one embodiment is depicted.

At210, the energy fundamental analysis module110may gather data about local weather patterns in the geographical area of the home. Data about weather patterns may include, but is not limited to, daily temperature graphs, chance of precipitation, and expected hours of sunlight. Local weather data may be gathered from the online databases of local weather services. Weather data may be parsed with a machine learning program that may include a natural language processing algorithm.

At220, the energy fundamental analysis module110may gather historical data about natural disasters in the geographical area of the home. Data about natural disasters may include, but is not limited to, seismic activity, flood warnings, fires, tropical storms and hurricanes. Natural disaster data may be gathered from online databases and may be parsed with a machine learning program that may include a natural language processing algorithm.

At230, the energy fundamental analysis module110may gather data about government policies and regulations affecting the utility costs of the home. Energy policy data may include, but is not limited to, tax incentives for installing solar panels, rates for electricity versus natural gas, and upcoming regulations affecting cost balancing of different utilities available to the home. Energy policy data may be gathered from online databases of news and government websites. Energy policy data may be parsed with a machine learning program that may include a natural language processing algorithm.

At240, energy fundamental analysis module110consolidates the local weather data210, natural disaster data220, and energy policy data230, and trains a machine learning algorithm that models and predicts the expected energy consumptions rates and costs for the home per hour of the day and day of the year. The machine learning model may be a deep learning time series model or a reinforcement learning model.

The time series deep learning model may be a Recurrent Neural Network (“RNN”), a Temporal Convolution Network (“TCN”), or a Long Short-Term Memory (“LSTM”). The reinforcement learning model may be a Deep Q Network (“DQN”). The energy fundamental analysis module110may train the RNN, TCN, LSTM or DQN based on the gathered data. The RNN, TCN, LSTM or DQN may identify expected energy consumption and costs per time of day and day of the year.

An RNN, TCN or LSTM is a type of neural network that may be well-suited to time series data. The neural networks may perform the same task for every element of a sequence, with the output being dependent on previous computations.

A DQN is a type of neural network that may be well-suited to sequential decision-making tasks. The network may learn optimal decision-making patterns through delayed reward optimization of sequential decisions.

Referring now toFIG. 3, an operational flowchart illustrating the exemplary energy consumption prediction module120used by the energy optimization system100according to at least one embodiment is depicted.

At310, the energy consumption prediction module120may gather historical hourly or15-minute home energy consumption data, manufacturer and model name for home devices with significant energy usage, and data on candidate new models to replace the current home devices. Hourly or15-minute energy consumption data may be gathered from the home's utility service providers. Data on current devices may be manually entered by the user. Data on candidate new models may be gathered from online manufacturer resources. Data on new models may be parsed with a machine learning program that may include a natural language processing algorithm.

At320, the energy consumption prediction module120may train a machine learning algorithm to model and predict energy consumption patterns for the home using the gathered energy consumption data310. The machine learning model may predict energy consumption per hour of the day and day of the year. The machine learning model may be a time series deep learning model or a reinforcement learning model.

At330, the energy consumption prediction module120may train a machine learning algorithm to identify which devices are responsible for the majority of energy consumption at each hour of the day and day of the year using the device data310and the output of machine learning model320. The machine learning model may be a time series deep learning model or a reinforcement learning model.

At340, the energy consumption prediction module120may use machine learning model330and the data on new models310to predict home energy consumption with various combinations of device upgrades. The module may produce a range of cost/energy consumption options with various device upgrade options.

Referring now toFIG. 4, an operational flowchart illustrating the exemplary personalized energy management module130used by the energy optimization system100according to at least one embodiment is depicted.

At410, the personalized energy management module130may predict energy cost and consumption with a range of device models using the device data125and the time series deep learning model or reinforcement learning model of the energy consumption analysis module120. Module410may produce a list of candidate device sets with predicted cost and energy consumption values.

At420, the personalized energy management module130may calculate expected upgrade costs and lifespans of the alternative device options proposed by module410using the gathered device data125. The upgrade costs and device lifespans may be used to refine the list of candidate device sets produced by module410.

At430, the personalized energy management module130may determine which device options are supported by the home's current utility configuration using the results of sub-module420and the gathered device data125. Supported devices may include those for which the home may need to perform utility upgrades. This may be used to refine the list of candidate device sets produced by module420.

At440, the personalized energy management module130may perform a cost/benefit analysis on the list of candidate device upgrade sets produced by module430. The cost/benefit analysis may optimize short, medium, and long-term costs and savings by weighing factors including, but not limited to, installation costs, tax benefits, utility costs and energy consumption rates. The cost/benefit analysis may be used to further refine the list of candidate device sets produced by module430.

At450, the personalized energy management module130may further refine the list of device sets produced by module440by taking into account the preferences of the home user. Preferences may be determined by examining the manufacturers and models of the current devices, or through manual user input.

At460, the personalized energy management module130may present the best options for short, medium, and long-term savings to the home user as produced by module450. The recommended devices, necessary utility modifications, and upgrade schedules may be provided to the user via display140of the energy optimization system100.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 5, illustrative cloud computing environment500is depicted. As shown, cloud computing environment500comprises one or more cloud computing nodes with which local computing devices used by cloud consumers, such as, for example, energy optimization system100, home devices, and automobile computer systems510, laptop computer520, and personal digital assistant (PDA) or cellular telephone530. Nodes may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described herein above, or a combination thereof. This allows cloud computing environment500to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices100,510,520, and530shown inFIG. 5are intended to be illustrative only and that the computing nodes and cloud computing environment500can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser)