Webinar on demand
Water Demand Forecasting with Machine Learning
hosted by TrendMiner
Duration: 53 minutes (40 minutes presentation + 13 minutes Q&A)
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Video Chapters Overview
00:00 Introduction and Webinar Agenda
The webinar focuses on the final installment of the Sustainability Labs series, delving into demand forecasting using machine learning within the context of sustainability and energy transition. It features insights from a Customer Success Manager and a Data Engineer from TrendMiner, who explore how advanced analytics can optimize efficiency and sustainability across various industries.
04:05 Common Industry Challenges
The webinar discusses the new challenges faced by various industries, primarily driven by environmental, social, political, and economic factors. The focus is on sustainability and how companies aim to operate in an environmentally friendly manner, addressing regulatory constraints, workforce adaptation, and overall efficiency.
05:46 Using Data to Drive Sustainability
This section highlights the importance of leveraging vast amounts of sensor-generated data to drive sustainability. It points out the lack of tools and knowledge for operators and engineers to extract meaningful insights from data, underscoring the potential of analytics solutions like TrendMiner to maximize data value for sustainable operations.
07:31 The Role of Water Management in Sustainability
The speakers emphasize the critical role of water management in sustainability efforts, discussing the interconnectedness of water usage across various sectors and the challenges posed by societal changes, such as those observed during COVID-19 lockdowns, which impacted water consumption patterns.
13:38 TrendMiner’s Key Capabilities
This chapter introduces TrendMiner’s key features, including its self-service analytics tool that allows process industry employees to conduct their data analysis. The presentation covers the different “hubs” within TrendMiner, such as Trend Hub, Context Hub, and Dash Hub, each serving a unique function in data analysis and visualization.
14:59 The Role of TrendMiner’s MLHub
The webinar presents the newly introduced Machine Learning Hub (MLHub) within TrendMiner, designed to bridge the gap between domain experts and data scientists. It allows for advanced analytics, including the use of Python scripts within a Jupyter notebook environment, to tackle complex data analysis tasks.
19:29 Use Case DEMO: Water Demand Forecasting
A detailed demonstration showcases how TrendMiner can be used for water demand forecasting. The use case involves analyzing historical water supply data, setting up monitoring systems for current water supply, and using machine learning to forecast future water demand, highlighting the software’s comprehensive capabilities in handling real-time data for predictive insights.
38:16 Conclusion
To conclude, this article will discuss the value of data-driven decision-making in accomplishing sustainability objectives and how TrendMiner’s cutting-edge analytics platform can help this process in a variety of industries.
39:06 Q&A
The final chapter provides an opportunity for the audience to ask questions and seek further clarification on any aspect of the presentation or TrendMiner’s functionalities.
Process manufacturing companies are increasingly exploring the use of digitalization and data science to meet their operational and sustainability objectives. One aspect of this involves empowering all employees to make decisions based on data, while another involves providing operational experts with access to data science tools without requiring extensive training.
In an upcoming webinar, we will demonstrate how machine learning models can be created and deployed to forecast water demand. This will be achieved by utilizing production data generated by sensors, as well as contextual data from other sources.
About the Speakers
With a degree in biochemical and chemical engineering and starting a new career as a Customer Success Manager for TrendMiner, Daniel Münchrath brings the two worlds of industry and data analytics together. Since joining TrendMiner in 2017, he has been supporting the DACH-division to grow and succeed in its efforts.
Sabine Pietruch is a Data Analytics Engineer at TrendMiner. She has a chemical engineering degree. As part of the Customer Success team in the DACH region, she supports TrendMiner users on their analytics journey and strives to close the gap between process engineers and data scientists.
