Jellyfish, a platform for creating and managing over-the-air sensors, surveyed their customer base about potential feature additions. Through a rigorous analysis of the feedback, a dominant preference emerged: users are seeking a feature focused on Energy Consumption Forecasting. This insight suggests that integrating such a feature would enable users to predict energy usage patterns, optimize their operations, and reduce costs. The solution delivered will develop and test this feature, ensuring it aligns with users' expectations. By addressing this need, JellyFish stands to further solidify its reputation as a customer-centric platform responsive to the evolving demands of its clients.

The Challenge:

JellyFish's current energy management platform is somewhat basic and doesn't meet the evolving needs of today's users. It shows energy consumption over set time frames but lacks the much-desired ability to predict future usage patterns. This gap doesn't just leave users wanting more; it prevents them from making well-informed decisions about their energy consumption.
As a result, users are left with a simple snapshot of their consumption habits, missing out on the opportunity to anticipate future patterns and make informed decisions. In essence, while the platform delivers on its basic promise, there's a clear gap between what it provides and the advanced, forecast-driven solutions that JellyFish's customers have voiced a desire for.

Moreover, given the clear feedback from recent surveys, it's evident that the platform's limited scope could be an obstacle in attracting more subscribers and maximizing profits. In response, JellyFish made a strategic decision to attach the power of AWS machine learning, to address these challenges comprehensively.

Why Zero&One and AWS:

Zero&One's collaboration with AWS creates a potent force in the realm of Machine Learning (ML) solutions. With a profound understanding of ML algorithms and techniques, it is well-equipped to tackle complex data challenges and extract valuable insights from large datasets. By partnering with AWS, Zero&One gains access to a reliable and scalable big data infrastructure, which is vital for ML applications that often require substantial computational resources. AWS's cloud-based services provide the necessary compute power and storage capacity to support ML model training, evaluation, and deployment on a vast scale. Furthermore, Zero&One's successful track record in delivering ML-driven projects ensures that organizations can entrust the data analytics initiatives to experienced professionals. The combined expertise of Zero&One and AWS establishes a robust foundation for organizations seeking to leverage the power of ML to drive innovation and data-based decision-making.

The Solution:

The proposed solution has put forward an innovative solution that promises to revolutionize the way energy consumption is monitored and predicted. This solution is an intricate mesh of advanced forecasting mechanisms combined with cutting-edge algorithms and comprehensive data analytics. At the heart of this solution is the capacity to present users with a detailed visualization of their energy consumption patterns using Amazon QuickSight.

However, what sets this system apart from traditional monitoring tools is its emphasis on foresight. By the power of machine learning the system provides accurate predictions about upcoming consumption trends using Amazon SageMaker. The algorithm used is DeepAR algorithm. This proactive approach ensures that users are always a step ahead, allowing them to anticipate, adapt, and make informed decisions regarding their energy use.

Results and Benefits:

The proposed solution leveraging AWS's SageMaker with DeepAR algorithm offers big benefits to JellyFish IoT, addressing the challenges of the number of subscribers, enhancing the user satisfaction and strengthen the platform market position. By incorporating such a system, JellyFish is not just meeting the immediate needs of its customers but also strategically positioning itself to attract a larger subscriber base. Anticipating energy consumption patterns is not only beneficial from an operational standpoint but also holds immense potential in terms of revenue generation. By addressing this explicit need, JellyFish stands to significantly boost its subscriber numbers and, in turn, witness a considerable uptick in its profit margins. The solution's deployment to bring about a significant transformation. They anticipate a remarkable increase in the number of subscribers on the platform by 15%. Moreover, the revenue increased by 5% during the first 6 months.