The Customer Engagement service provides NET2GRID customers, i.e., service providers, the means to interact with their customers about their energy behavior via digital channels. These consumers are the end-users of this Customer Engagement API. The goal is to enable app developers to create a compelling service that enhances awareness of users about their energy behavior and enhances the depth of the relationship and loyalty between the service provider and their customer. The API opens up a broad set of actionable energy insight features for this purpose.
Energy Disaggregation breaks down the overall daily/weekly/monthly consumption measurements into the participating appliances/activities. This is realized using various Machine Learning, Artificial Intelligence, and Time Series Analysis techniques. The primary goal is to help end-users reduce their electricity bills and carbon footprint and understand how the appliances inside their household are consuming energy.
Insights Service is responsible for comparing the energy disaggregation results of a household with the historical consumption of the same home or the consumption of other households in the same region with the same demographic profile. This can add more context to the results of the energy disaggregation since a single consumption cannot help the end-user understand if the consumption of an appliance is low or high.
Recommendation Services translate the results of the energy disaggregation and insights services into actions that the end-user can follow in order to improve their energy consumption practices. This means there are specific, individualized recommendations for household appliances and activities that participate heavily in household consumption.
It is essential to understand that the end customer’s behavior may be the reason for increased daily/monthly consumption due to his consumption behavior. But another factor plays a vital role in this quest for energy consumption reduction. If, for example, the Washing Machine appliance installed within a household is old and energy inefficient, changing one's behavior will not solve the problem completely.
The cost calculation engine handles tariff information related to the contract of the end user with the energy provider and converts the energy consumption or production to cost or revenue accordingly. It can be applied to the following types of prosumers:
Single Rate Tariffs: Users are billed based on a single tariff throughout the day.
Time of Use (ToU) Tariffs: Users who are billed with a different rate on different parts of the day. Typically one rate corresponds to the daily while the other to the nightly consumption.
Feed-in (Injection) Tariffs: Prosumers who produce electricity and return it to the grid. The revenue can be calculated with either a single rate feed-in tariff or ToU feed-in tariff.
Relevant use cases:
- Provide insights into the costs of produced and consumed energy over a period.
- Provide insights into the consumption costs of various appliances.
- Provide end users with information on currently applicable tariffs based on their contract.
Energy conservation and emission reduction have always been a hot topic for society, but it has become necessary in recent decades. However, persuading individual consumers to save energy is still challenging due to the lack of detailed energy consumption information. It has been shown that active energy data feedback to energy users can reduce about 5-20% of energy.
A typical approach to obtain meaningful feedback that may lead to energy-saving actions is Real-time Load Monitoring (LM). This method can push toward more effective energy management strategies, such as energy efficiency programs, demand-side management, and peak load shedding. LM can be performed in two ways: (a) the hardware-centric and (b) the software-defined approach. The former solution requires a high cost associated with hardware materials and labor effort. The latter solution is Non-Intrusive Load Monitoring (NILM); it is easy to install and requires no or low maintenance; thus, it can scale gracefully.
NILM is a convenient means of determining the energy consumption and the state of operation of individual appliances within an installation by analyzing the aggregate load measured on its main power meter. More specifically, it analyzes changes in the voltage and current and deducts what appliances are switched on or off and each appliance's end-use energy consumption. It is non-intrusive because it does not require intruding into the consumer premises to measure the individual appliances’ consumption.
Updated 3 months ago