Abstract: This paper presents a broad view of management of design and implementation of power systems for Data Centers. The paper outlines many challenges that are present because of the demanding requirements of Data Centers both in design and management, then introduces opportunities that recent technological advances have made possible. This paper presents several new approaches of ownership and responsibilities that directly affect financial viability of the Data Center.
I’m in charge of dinner tonight, so Little Miss H and I are going to make Tomato Soup and Grilled Cheese sandwiches! It’s such an easy dinner that she can do almost all of it herself, I’ll just need to talk her through it. Find my recipe in the comments! pic.twitter.com/GVAi3t0I8f
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Since so much of what we do in standards setting is built upon a foundation of a shared understanding and agreement of the meaning of words (no less so than in technical standard setting) that time is well spent reflecting upon the origin of the nouns and verbs of that we use every day. Best practice cannot be discovered, much less promulgated, without its understanding secured with common language.
Because electrotechnology changes continually, definitions (vocabulary) in its best practice literature changes continually; not unlike any language on earth that adapts to the moment and place.
The changes reflect changes in technology or changes in how the technology works in practice; even how the manufacturers create adaptations to field conditions by combining functions. Any smart electrical component has a digital language embedded in it, for example.
Consider the 2023 National Electrical Code. Apart from many others the NEC will contain a major change to Article 100 (Definitions); the subject of elevated debate over the past three years.
When we refer “language” we must distinguish between formal language, informal language, colloquial language and dialect which may differ the language spoken, language written at the office and language used on the job site. “Terms of art”
Are these terms (or, “terms of art”) best understood in context (upstream articles in Chapters 4 through 8) — or should they be adjudicated by the 14 Principals of Code Making Panel 1? The answer will arrive in the fullness of time. Many changes to the National Electrical Code require more than one cycle to stabilize.
Code Making Panel 1 has always been the heaviest of all NEC panels. As explained n our ABOUT, the University of Michigan held a vote in CMP-1 for 20+ years (11 revision cycles) before moving to the healthcare facilities committee for the IEEE Education & Healthcare Facilities Committee. Standards Michigan continues its involvement on behalf of the US education facility industry — the second largest building construction market. There is no other pure user-interest voice on any technical committee; although in some cases consulting companies are retained for special purposes.
To serve the purpose of making NFPA 70 more “useable” we respect the Standards Council decision to make this change if it contributes to the viability of the NFPA business model. We get to say this because no other trade association comes close to having as enduring and as strong a voice: NFPA stands above all other US-based SDO’s in fairness and consideration of its constituency. The electrical safety community in the United States is a mighty tough crowd.
If the change does not work, or work well enough, nothing should prohibit reversing the trend toward “re-centralizing” — or “de-centralizing” the definitions.
Public comment on the First Draft of the 2026 Edition will be received until August 28, 2024.
Technical Committees meet during the last half of October to respond to public comment on the First Draft of the 2026 National Electrical Code.
Julia is a programming language that has gained popularity in the field of artificial intelligence (AI) and scientific computing for several reasons.
High Performance: Julia is designed to be a high-performance language, often compared to languages like C and Fortran. It achieves this performance through just-in-time (JIT) compilation, allowing it to execute code at speeds close to statically compiled languages. This makes Julia well-suited for computationally intensive AI tasks such as numerical simulations and deep learning.
Ease of Use: Julia is designed with a clean and expressive syntax that is easy to read and write. It feels similar to other high-level languages like Python, making it accessible to developers with a background in Python or other scripting languages.
Multiple Dispatch: Julia’s multiple dispatch system allows functions to be specialized on the types of all their arguments, leading to more generic and efficient code. This feature is particularly useful when dealing with complex data types and polymorphic behavior, which is common in AI and scientific computing.
Rich Ecosystem: Julia has a growing ecosystem of packages and libraries for AI and scientific computing. Libraries like Flux.jl for deep learning, MLJ.jl for machine learning, and DifferentialEquations.jl for solving differential equations make it a powerful choice for AI researchers and practitioners.
Interoperability: Julia offers excellent interoperability with other languages, such as Python, C, and Fortran. This means you can leverage existing code written in these languages and seamlessly integrate it into your Julia AI projects.
Open Source: Julia is an open-source language, which means it is freely available and has an active community of developers and users. This makes it easy to find resources, documentation, and community support for your AI projects.
Parallel and Distributed Computing: Julia has built-in support for parallel and distributed computing, making it well-suited for tasks that require scaling across multiple cores or distributed computing clusters. This is beneficial for large-scale AI projects and simulations.
Interactive Development: Julia’s REPL (Read-Eval-Print Loop) and notebook support make it an excellent choice for interactive data analysis and experimentation, which are common in AI research and development.
While Julia has many advantages for AI applications, it’s important to note that its popularity and ecosystem continue to grow, so some specialized AI libraries or tools may still be more mature in other languages like Python. Therefore, the choice of programming language should also consider the specific requirements and constraints of your AI project, as well as the availability of libraries and expertise in your development team.
ABSTRACT. Many optimization problems in power transmission networks can be formulated as polynomial problems with complex variables. A polynomial optimization problem with complex variables consists in optimizing a real-valued polynomial whose variables and coefficients are complex numbers subject to some complex polynomial equality or inequality constraints. These problems are usually directly expressed with real variables. In this work, we propose a Julia module allowing the representation of polynomial problems in their original complex formulation. This module is applied to power system optimization and its generic design enables the description of several variants of power system problems. Results for the Optimal Power Flow in Alternating Current problem and for the Preventive-Security Constrained Optimal Power Flow problem are presented.
There are never too few people to make a decision in academia: With the approval of the ‘Capital Planning Board’, ‘Campus Dining & Shops’ brings a new dining experience as part of the ‘Branding Office’s’ ‘Heart of the Campus initiative’; mimicking a trend that converges the family kitchen into a library experience.
The ‘Office of Student Life’ played an integral role in all phases of the project and has prepared this virtual drone tour.
This paper presents an analysis of energy savings in typical university campus buildings due to Daylight Saving Time in Mexico. The electricity demand load profiles are analyzed in five facilities of the National Autonomous University of Mexico central campus. Each facility presents different demand characteristics according to its usage. Demand data have been obtained through electrical measurements using Survalent ONE SCADA system®. The last week winter period demand profile compared to the first week summer period demand profile are shown and analyzed. Results have shown DST effects on energy consumption in university facilities. With these results, it is also possible to develop decision-making programs to drive energy efficient plans in university campus. In addition, this information can be used to promote efficient and clean energy micro grids. Including ocean energy generation for isolated communities.
New update alert! The 2022 update to the Trademark Assignment Dataset is now available online. Find 1.29 million trademark assignments, involving 2.28 million unique trademark properties issued by the USPTO between March 1952 and January 2023: https://t.co/njrDAbSpwBpic.twitter.com/GkAXrHoQ9T
