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Cognitive Solar Energy System Disclosure Number: IPCOM000245382D
Publication Date: 2016-Mar-04
Document File: 2 page(s) / 68K

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The Prior Art Database


Disclosed is a cognitive solar energy system that uses predictive analytics to calculate the amount of energy that a household or business consumer requires over a given period. The system then makes accurate predictions of expected solar energy output, allowing the consumer to prepare, thus ensuring that the client’s energy needs are not compromised.

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Cognitive Solar Energy System

Solar panels are a source of renewable energy. A major drawback with solar panels is

the perceived unreliability of the energy output. Current estimation and survey models estimate whether a specific location is a good location for a solar panel installation. These models do not provide a real-time predictive system that combines the concept of Internet of Things (IoT) and Cognitive Computing, which can allow the customer to

roughly estimate the expected amount of energy generated from a solar panel installation for any given week. Furthermore, current methods do not match the forecasted output with the consumer's energy usage, thereby neglecting crucial insights. This reduces the efficiency and perceived reliability of solar energy as a singular source of power for a given household or business.

Assume a scenario in which a manufacturing business that heavily relies on a

consistent source of energy is trying to migrate to a primarily solar energy based system. Feasibility studies show that, given the fact that the location has had consistent sun cover in the past two years, it is a good candidate for solar panel installation. While on average, this may be enough, the business needs to have real time updates on the expected output from the solar panels. If, for one week, an unexpected weather condition undermines the solar energy productivity, and the business is not prepared for it, then its daily operations may be severely compromised.

If the business has a system that estimates in advance the expected solar energy output for that particular week, then the company can prepare for the reduction in solar energy and ensure that there is no disruption to the energy outflow. Additionally, by calculating the amount of energy that the company expects to require for that given

week, the company knows exactly the energy shortfall and can find alternatives to address it.

To consider solar energy as a viable energy source, some sort of insurance is needed for the rough estimate of solar energy output.

The novel contribution is a cognitive solar energy system. The system combines two disparate sources of information: an accurate visualization of how much energy is needed for an upcoming period and how much of those energy needs can be addressed by solar panels during that period. This allows the customer to be better prepared with alternatives as well as more efficient in energy utilization.

The system is integrated in any setting with established has solar panels. Ideally, it should be in an environment where a great amount of energy is required for the household or business to function. This furthers the importance of the predictive component of the solution to ensure that the daily energy needs are met.

This implementation leverages the potential of content analytics, a partnership with a

weather company, space in the IoT, and any integrated solar panel system in a household or business setting. This soluti...