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Check out Energize Capital's insightful report on "The Role of Digital Infrastructure in Climate" with a focus on Data Management. A must-read for those passionate about clean energy technology and the data space. 💡 Data Management Investment Thesis from the report: ⏩ Improvements in compute power have enabled the capability to run computation on industrial edge devices. Today, software is needed to orchestrate the connection of end devices to the cloud. The pain point becomes more acute as the number of edge / IoT devices grows and the capabilities for edge ML increase. ⏩ Climate assets are growing in volume in remote locations. Operating these assets requires internet connectivity for remote control from the HQ. Secure, reliable networks are a must-have for utility-scale climate asset deployment. ⏩ New digital infrastructure solutions coming from disparate carbon asset data sources are needed to aggregate, store, and enable the manipulation of real-time data at scale. Unlike coal or gas fired power facilities, renewable energy facilities are remote-operated from the corporate HQ. ⏩  Highly regulated, risk-adverse industries like renewable energy development require detailed engineering, excess documentation, permitting, and supply chain redundancies. These corporate soft costs can be automated at the back office with new robotic processing software. #CleanEnergy #DataManagement #ClimateTech

HOW CAN AI IMPACT SUSTAINABILITY IN DATA CENTERS. AI (Artificial Intelligence) brings lots of practical improvements to data center sustainability by applying smarter mathematical models and learning algorithms to a wide range of data center tasks. Here are some areas AI can significantly impact sustainability in data centers: 1. Energy Efficiency. AI reduces energy consumption by utilising intensity and flexibility in control of operations, loads, and electricity supply infrastructures. Advanced power condition algorithms used by machines for predicting the time periods of high load and increases in power requirements help to cut power wastage. 2. Predictive Maintenance. AI indicates when the equipment may be due for failure to prevent more damage, and fix the problem before downtime occurs. This being as true as it may seem it helps improve the number of years of usage that the data center equipment can serve besides reducing the usage of resources. 3. Resource Management. AI improves resource planning in terms of the distribution of server usage and general load. This load balancing which also helps to distribute loads reduces the number of hosts to be active, thus saving power. 4. Cooling Optimization. They manage and control the various means for cooling of the environment in order to keep it as comfortable as possible using the least energy possible as will be illustrated in the subsequent sections. This helps to minimize convective cooling which is very energy intensive in data hubs thereby saving on energy. 5. Renewable Energy Integration. AI can help better control and coordinate renewable energy sources, and their supply of the mentioned energy to the data center by anticipating the availability of sources and integrating them into the energy mix of the data center more optimally. That is; this helps in eradicating the use of energy sources such as fuel, coal and the likes which are scarce. 6. Carbon Footprint Reduction. Due to the incorporation of AI in smart scheduling and energy consumption, data centers are able to use the limited resources efficiently thus reducing emissions rate. They can also map emissions and report progress to authorities in relation to set environmental conservation laws. 7. Operational Efficiency In data center management, the use of AI leads to a decrease in the number of times people are needed to set up systems that are time-consuming since they usually increase energy consumption due to inefficiencies. Sustainability in data centers can be promoted through AI in terms of efficient energy consumption, forecasting of maintenance requirements, resources usage, and driving the implementation of renewable energy resources. These result in lower energy use, lower operating expenditure, and less energy intensity that in turn causes less harm to the environment.

Katalytx promotes Sustainable Solution Architecture that not only meets functional requirements but also contribute to a sustainable future The Golden rules for sustainable software development encompass principles and practices aimed at reducing the environmental impact of software throughout its lifecycle Design for Efficiency Design software architectures and systems with a focus on energy efficiency and resource optimization. Consider factors such as hardware selection, data storage mechanisms, and system scalability to minimize environmental impact. Prioritize Performance Aim for high performance in software applications to minimize energy consumption and maximize efficiency. Optimize code for speed and responsiveness, and implement caching, batching, and other performance-enhancing techniques to reduce resource usage. Optimize Resource Usage Strive to minimize the consumption of computing resources such as CPU cycles, memory, storage, and network bandwidth. Use efficient algorithms, data structures, and coding practices to optimize resource usage and reduce energy consumption. Reduce Waste Minimize waste generation throughout the software development lifecycle, including code, data, and infrastructure. Adopt lean development practices, eliminate unnecessary features and code bloat, and optimize data storage and transmission to reduce waste. Promote Sustainability Awareness Align to the Organizations overall sustainability strategy. Educate stakeholders, including developers, architects, project managers, and end-users, about the importance of sustainability in software development. Embrace Renewable Energy Choose hosting providers and data centers that prioritize renewable energy sources such as solar, wind, and hydroelectric power. Support initiatives to transition to renewable energy and reduce the carbon footprint of software development operations. Practice Lifecycle Management Implement strategies for managing the entire lifecycle of software, from planning and design to deployment, maintenance, and decommissioning. Plan for end-of-life considerations, including responsible disposal, recycling, or repurposing of hardware and software components. Measure and Monitor Implement tools and metrics for measuring and monitoring the environmental impact of software development efforts. Track energy consumption, resource usage, carbon emissions, and other sustainability metrics to identify areas for improvement and optimize performance over time. Continuous Improvement Commit to continuous improvement in sustainable software development practices. Set goals, benchmark performance, and regularly assess progress towards sustainability objectives. Iterate on strategies and initiatives to drive ongoing environmental stewardship and innovation. #sustainability #esg #itconsulting #itserviceprovider #enterprisearchitecture #softwaredevelopment #cxo

ANALYSIS OF Q1 JUNE 2024 GOOD RESULTS SECTOR IT SOFTWARE MEDIUM AND SMALL COMPANIES 15 CEINSYS LTD PART 2 , Ceinsys took a strategic decision to foray into the mobility services space via its acquisition of a specialized engineering service provider - AllyGrow Technologies in FY22 which has a good international presence. • AllyGrow’s “Manufacturing Solutions” span the entire product development process – covering both the product engineering activities and industrial automation solutions for various verticals such as two / three-wheelers, passenger cars, commercial vehicles, and off-highway equipment. • The company boasts a marquee list of customers ranging from large corporates, OEMS, asset management companies and government bodies in the Geospatial and Manufacturing sectors, globally. • Ceinsys Tech Limited now is a technology-driven organization that specializes in offering Geospatial, Enterprise and Engineering solutions by providing independent opinions, actionable insights, and efficient solutions to customers across private and government enterprises across the utility, infrastructure, natural resources, and Manufacturing sectors It has a global presence with offices in India, the United States, United Kingdom and Germany. • The company’s market-leading solutions empower customers to achieve their goals, overcome challenges and drive success in their respective industries, by providing them tools, technologies, and expertese they need to excel and stay ahead of their competition. • The company is also into software product development, Artificial Intelligence (AI), Machine Learning (ML) and Embedded Electronics space through a new vertical formation which focuses on product development activities related to Metaverse, Ed-Tech, Gaming and Mobility Segments Covered Geospatial & Engineering Services A Water Holistic water management solutions that ensure Sustainable management, development, and utilization of water resources & infrastructure B Energy GIS enabled strategies that help efficient design, management, planning, installations and monitoring C Transport Effective planning, accelerated digitalization, improved safety, standardized & streamlined execution of road transportation projects. D AEC Adding location context to AEC projects for improved workflows, and increased collaboration throughout project life cycles. 10 Mn Consumer indexing • 87,700 Miles Network Mapping • Managed Data services for 587+ Towns • Enterprise GIS implementation for 3 states • Infrastructure development 30+ substations • SCADA implementation for India’s biggest transmission Utility Point Cloud: 20 Mn • Scan to Model: 3,230k+ Sq Ft • Enterprise GIS • Digital Project Mgmt System • Decision Support Systems

Crucial to #industrialsustainbility and #energytransition. Look for more in the near future from the ARC Advisory Group team covering these wide-ranging, major market factors.

Business Architecture in the Energy Sector. From Watts to Insights: Transform Your Energy Business with a Strategic Data Model Enterprise/Business Architects, your energy data is a valuable asset. Unlock its full potential with a business data model that transforms raw data into actionable insights. How: The Energy Data Insights Framework (EDIF): 1. Asset Performance: Model your energy assets, such as power plants, substations, and transmission lines, to track their performance, predict maintenance needs, and optimize operations. 2. Customer Engagement: Capture customer data, including demographics, energy usage patterns, and preferences, to personalize services and improve customer satisfaction. 3. Grid Management: Model grid infrastructure and operations to identify bottlenecks, optimize load balancing, and enhance grid resilience. 4. Regulatory Compliance: Ensure your data model aligns with evolving regulatory requirements and industry standards. Success Factors: • Scalability: Design your model with flexibility to accommodate new data sources, technologies, and business models. • Data Security: Implement robust security measures to protect sensitive data, such as customer information and grid infrastructure data. • Collaboration: Foster collaboration between data architects, business analysts, and domain experts to ensure a comprehensive and accurate data model. A strategic business data model can transform your energy business, enabling you to gain a competitive advantage, improve operational efficiency, and deliver superior customer experiences. Turn your data into a powerful asset. To architect your business right, consider our products. (Important: Please read the product descriptions carefully, as the products' content will differ from the social posts' content.)

Bredec Group Bredec Group Show HN: SolarChats – LLMs Integrated into Project Management: A few months ago I started exploring OpenAI's API, with experimental integration of ChatGPT4 into a project management system I'd written. As that progressed, I found giving the LLMs a specific expertise made them better assistants, so I remade my project into a home solar energy adoption R&D and planning resource. That purposeful limiting of the scope of the project appears to have made the LLM chatbots more directed and significantly better assistants. So, the project continued with a solar adoption focus... SolarChats.com is a series of personality chatbots integrated into traditional project management software, such as text chats, memos, spreadsheets, document sharing, and project privacy. The LLM integration provides voice to text, solar expert chatbots, a creative writing professor chatbot, a finance expert chatbot, a spreadsheet authoring expert chatbot, a negotiations chatbot, and secondary behind the scenes analysis bots that analyze chat conversations for the quality of the human communications and use that to moderate the other chatbots to include more explainers in their replies because the human does not appear to be comprehending, or shorten the replies because the human appears to understand what they are doing. The chatbot/AIs do not do one's project, they provide expert guidance while you plan whatever your solar project happens to be. Some of the chatbots are also downright silly. But their advice is still expert. The chatbots with personality are only in the top level chat interface used for initial research, and once one has information that has graduated from a chat conversation to a formalized memo of information you want to retain, when editing a memo (or spreadsheet) the chatbots are strictly business, assisting without personality. I'm interested in what people think, and I am interested in discussion with others that may be working along the same lines. And, yes, I am aware the site looks like it's from 2007. A slick web design is not the point right now... --- Comments URL: https://lnkd.in/dUPgCCJN Points: 1 # Comments: 0 info@bredec.com Inquiry@bredec.com

#GUTENBRaiN is an AI-powered digital engineering interface developed by Axians and Leonard that allows organizations to manage efficiently technical information of large engineering assets, like power plants, substations, refineries or oil platforms. It was developed to tackle 3 main #operationalchallenges: ⚡Document Ingestion - The need to extract the embedded data in engineering files. A large scale project is composed of thousands of dispersed documents, from disparate sources and different file formats that ultimately need to comply with the project’s specifications. GUTENBRaiN can process large amounts of data from various file formats into a managed database. Additionally, these documents/files can include text, tabular data and technical drawings, sometimes stored in legacy formats. Since this is not directly usable, GUTENBRaiN employs industry-leading Vision AI to analyze the contents with high prediction accuracy. ⚡⚡Extracting Relevant Asset Characteristics From Files - To assist teams when performing multiple activities, GUTENBRaiN applies powerful pattern matching algorithms that can detect and extract all relevant assets from these files, to produce a valuable and readily accessible knowledge base that allows teams to quickly locate mission-critical information. ⚡⚡⚡Tackle the Complexity of Managing Multiple Changes and Updates in the Project Documentation - GUTENBRaiN can automatically reprocess any document that is updated and signal the changes between different revisions or versions of the same document. This allows teams to quickly understand variances and make more informed decisions when significant readjustments are detected, thus preventing potential costly business mistakes.

In a landmark move towards advancing environmental sustainability in the data center industry, Schneider Electric has released comprehensive insights and strategies to tackle the burgeoning challenge of Scope 3 emissions. As data centers pivot towards renewable energy sources, Scope 3 emissions have emerged as the predominant contributor to their greenhouse gas (GHG) output. https://lnkd.in/dbPb4zEB Schneider Electric Danish Data Center Industry Norwegian Data Center Industry Swedish Data Center Industry Data Center Nation Vantage Data Centers Scala Data Centers NVIDIA Data Center Cisco Data Center and Cloud CAI Data Center Services Data Center ISH Datagoogler Datavant Data Engineering Data Science Learner Community Data Innovation Summit Data Visualization Data Management Data Governance Microsoft AI Cloud Partner Program

(Full report available to ARC clients) Applying a System of Systems Philosophy for Sustainability Success https://lnkd.in/gm8ZNwTE The drive to sustainability, decarbonization, and energy transition is creating unique challenges for industrial and critical infrastructure end users. Unlike traditional industrial operations, where the worlds of enterprise and operations were largely separate, the drive to sustainability and energy transition requires unprecedented levels of integration of both enterprise and operations data from a wide variety of disparate systems. Sustainability is changing the requirements for end-user access to data. Sustainability-related initiatives and their related metrics can draw from widely disparate sources of data, including financial, supply chain, operations management, and real-time control data. In the past, data flows in organizations were fairly hierarchical, data from operations was shared with the enterprise for various business functions. Conversely, enterprise-level data was driven down to operations for things like production planning, scheduling, and other functions. Sustainability and energy transition initiatives cannot be supported with this traditional hierarchical structure. The push to sustainability represents the final step toward a holistic environment that can integrate data from any point or system in the organization, regardless of whether it is IT/enterprise or OT-centric. Not surprisingly, sustainability initiatives usually go hand in hand with digital transformation programs. New technologies must be implemented to attain goals of open data access and contextualization, and this requires industrial-grade data fabrics. It’s also important for end users to adopt a systems engineering mindset for sustainability. Having access to data and an appropriate data fabric is important, but this data must also be systematized, with a single pane of glass for visualization, reporting, analytics, the application of AI, digital twins, and other advanced technologies. With a system of systems, the whole is truly greater than the sum of its parts. System of systems also allows existing systems within the organization to remain autonomous and does not require a significant investment in replacement.