Data-Driven Load Optimization

Experienced Data Analyst || Data Visualization Expert - Power BI Developer || Python Analyst || Operations Analyst || Research and Policy Analyst

I took this report’s load time from 10-15 seconds to less than 1 second.. and reduced its model size from 192 MB to just 20 MB, approximately 90% reduction! For the Fabric User Group Nigeria September Challenge. The business problem was to optimize a slow-loading executive dashboard for Van Arsdel that was causing significant productivity and confidence issues. Leveraging Semantic Link Labs, my core actions were: 📍Streamlined Data Model & Query Steps: I used Power Query to disable unused tables and eliminate unreferenced columns, which was a key factor in reducing memory footprint. 📍Optimized Relationships: I replaced a problematic many-to-many relationship with an efficient one-to-many setup using a bridge table and switched to single-directional filters to improve query performance. 📍Disabled Auto Date/Time: This feature adds hidden, resource-intensive calendar tables. Turning it off immediately made the model leaner. 📍Refactored DAX: I replaced inefficient DAX measures that were forcing multiple table scans with streamlined, standard time intelligence functions like DATEADD, resulting in significant performance gains. Business Impact? The improvements I made directly addressed the business's pain points: ✅Increased Productivity: Executives now save 2-3 hours per week with a fast, responsive dashboard, allowing them to focus on strategic tasks rather than waiting for data to load. ✅Faster Decision-Making: The dashboard is now a reliable tool for quarterly planning, eliminating the delays that were affecting the business. ✅Restored Stakeholder Confidence: The dashboard now loads instantly, ensuring smooth, professional board presentations and reinforcing confidence in the data and the team behind it. For more detail, read repo: https://lnkd.in/dGBc4gCy

Transforming Facilities with Strategic HVAC Optimization and BAS Integration! Kelso Your Building’s Reliability Partner

Setting up trending on a BAS (Building Automation System) network to minimize bandwidth consumption while providing real-time access to data involves a strategic approach to data collection, storage, and retrieval. Here are the steps to achieve this: 1. Adjust Polling Intervals: Set polling intervals based on the criticality and variability of the data. For less critical data, use longer intervals. Event-Driven Polling: Use change-of-value (COV) polling instead of periodic polling for data points that change infrequently. This means data is sent only when a change occurs. 2. Local Aggregation: Aggregate data locally at the field controllers before sending it to the central station. This reduces the amount of data sent over the network. Hierarchical Trending: Use a hierarchical trending approach where data is collected and stored at multiple levels, such as field controllers, supervisory controllers, and the central station. 3. Data Compression: Utilize data compression techniques to reduce the size of the data being transmitted. Niagara Framework supports various data compression methods. Delta Compression: Only send the difference (delta) between the last reported value and the current value. 4. Trend Only Essential Data: Identify and trend only the most critical data points. Avoid trending points that provide little value or insight. Trend Filtering: Apply filters to trend logs to limit data to specific ranges, times, or conditions. 5. Use Historical Databases: Store historical data in an optimized database designed for time-series data. Niagara typically uses the built-in history database, but you can also integrate with external databases. Data Archiving: Implement a data archiving strategy to move older data to long-term storage, reducing the load on the primary database. 6. Data Caching: Cache data locally on the client side to reduce the need for repeated data requests. WebSockets and Push Notifications: Use WebSockets or other push notification mechanisms to provide real-time updates to clients without constant polling. 7. Segment the Network: Use VLANs or other network segmentation techniques to separate BAS traffic from other network traffic, ensuring optimal performance. Quality of Service (QoS): Implement QoS policies to prioritize BAS traffic on the network. 8. Regularly Review and Adjust Trends: Periodically review the trends and adjust configurations as needed based on the usage patterns and network performance. Monitor Network and System Performance: Continuously monitor the network and system performance to identify and address any bottlenecks or issues. By implementing these strategies, you can ensure that the trending on your BAS network is efficient in terms of bandwidth consumption while providing real-time access to critical data for end users.

Universitätsprofessor | Wissenschaftler | Speaker | Güterverkehr & Transport | Technische Universität Darmstadt

Demand for combined road/rail transport in Germany is expected to grow, with some forecasts projecting an increase of 2.9 million tonnes already this year. But can the system keep up? Extensive maintenance works, rising costs, and declining reliability are already putting the system under pressure. And every additional truck on the road means more congestion, more accidents, and more strain on both infrastructure and drivers – making transport planning more uncertain. At the same time, we cannot simply expand rail freight infrastructure without limit. If rail is to play a stronger role, we need to make better use of the capacity we already have. That means one thing in particular: trains need to run as full and as efficiently as possible – improving performance while reducing costs. This is where data-driven planning and artificial intelligence come into play. A case study from our research at Technische Universität Darmstadt shows what is possible when train utilization is systematically improved by combining AI-based forecasting methods with mathematical optimization: ⚪ travel times can be reduced by up to 15% ⚪ train utilization increases by around 1.7 percentage points ⚪ and the number of required train connections drop by roughly 8%. Together, these effects can lead to smoother operations, fewer bottlenecks, and a shift of freight traffic to rail – delivering tangible progress and contributing to climate goals. Find out more in the comments 👇 #LogResearchTUDA

The data center buildout is about to create one of the most compelling economic cases for load flexibility the energy industry has ever seen — and most people aren’t talking about it yet. Tens of gigawatts of new AI-driven load are coming online backed by high heat rate reciprocating gas engines. These units are presented as a bridge — fast to permit, fast to deploy, fast to get load connected. But they are expensive to run. Once that load is live and those engines are the thing holding the lights on, operators are going to be staring at fuel bills that make demand flexibility look extraordinarily cheap by comparison. The merit order math is unforgiving: when your marginal unit costs $150/MWh or more to operate, any resource that can reduce net load at that margin has enormous value. This is where load flexibility wins — not as a sustainability story, but as a pure economics story. You don’t need to build anything to tap into it. The capacity is already embedded in the load itself. Shift a training job by two hours. Throttle a cooling system during a peak window. Sequence GPU workloads to avoid coincident peaks. The avoided cost of not running that reciprocating engine is the value signal, and it’s a strong one. At Octopus, we’ve long believed that load flexibility is the fastest and cheapest form of capacity — and the data center buildout is about to prove that at massive scale. What we are entering is a period of structural, not episodic, pressure to find alternatives to expensive marginal generation. The operators and utilities who build flexibility into their data center strategies now — rather than treating it as an afterthought — will have a significant cost advantage. The ones who don’t will spend the next decade paying for it, one expensive engine-hour at a time.

Chief Growth Officer, Hyper Scale Data Centers and IBRs| MicroGrid, Controller, DigitalTwin | ERCOT PJM MISO SPP AESO IESO PSCAD PSSE SCADA HMI PPC

Based on the latest #ERCOT #Large #Load Working Group discussions on February 19, a proposed approach was introduced to evaluate the impact of #AI #data_center loads on the grid. At this stage, it has been suggested that AI loads limit their power variations within a defined time window. The current proposal considers a 5-second window with a maximum allowable load swing of 10 MW. The concept of repetitive load variations was also discussed, indicating that sustained or repeated load swings might be the main reason for the concern, not just a single power jump. Based on our recent observations and discussions with developers, many are leaning toward addressing these requirements through corrective actions at the facility level, particularly by #colocating #battery #energy #storage systems with the data center to smooth load variations. The key observations at this stage are the following: Energy storage can be an effective solution for mitigating load swings, but there is always a response #delay between the #detection of the load variation and the corrective action from the storage system. We are talking about a delay as low as 10-20 ms. Because of this delay, fast power jumps during the first few cycles of the load change may still appear at the grid interface. Regardless of the size of the battery system, this very first jump cannot be completely eliminated because it is driven by control and measurement delays (i.e. even oversizing BESS unit may not resolve the issue) Our studies have indicated that #full-#conversion solutions, where the load is fully #decoupled from the grid through power electronic interfaces, can address these variations more effectively. However, these solutions come with additional cost (but a great tool to significantly reduce the project operation #risks) As the industry evolves and the first wave of large AI load facilities begins to interconnect to the grid, the industry will gain better visibility into the actual system behavior. At that point, ERCOT and other stakeholders will be in a stronger position to determine appropriate requirements, including acceptable #damping #ratios, maximum load variation limits, and the most effective #mitigation methods. Every millisecond of latency should be accounted for when selecting the size and technology for AI load smoothing, even at the very early stages. That is why we are moving towards Real Time Simulation when clients ask us about the amount of storage they need. Even a small delay can lead to huge financial risks.  

Chief Information Officer | AI Infrastructure, Data Center Transformation & IT Operations

𝗛𝗼𝘄 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆 𝗤𝘂𝗮𝗻𝘁𝘂𝗺-𝗜𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝘁𝗼 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗔𝗜𝗢𝗽𝘀 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿) Most leaders hear “quantum” and think of it as experimental, expensive, and years away. That’s a mistake. Quantum-inspired algorithms run on classical infrastructure today and solve the hardest problem you actually have: large-scale optimization under constraints. If you run data centers, this is immediately actionable. What they actually do They convert your environment into an energy minimization problem. Instead of brute forcing every possibility, they rapidly converge on high-quality solutions across massive decision spaces. Think: • Placement • Scheduling • Routing • Thermal balancing • Power allocation Where to apply first (high ROI use cases) 1. Rack and cluster placement Model racks, power domains, cooling zones, and network topology as constraints. Objective: minimize latency + cable length + thermal hotspots. 2. GPU scheduling and utilization: Encode job priority, SLA windows, GPU affinity, and network contention. Objective: maximize utilization while reducing idle burn and queue latency. 3. Thermal + power balancing: Integrate cooling capacity, airflow constraints, and power density. Objective: flatten hotspots without over-provisioning. 4. Network traffic shaping Model east-west traffic flows and oversubscription ratios. Objective: Reduce congestion and packet loss under peak load. How to implement (practical workflow) Step 1: Define variables • Binary: placement decisions, routing paths • Continuous: load, temperature, power draw Step 2: Define constraints • Power caps per rack and row • Cooling limits by zone • Network bandwidth ceilings • SLA requirements Step 3: Build the objective function. Combine into a weighted cost function: • Latency • Energy consumption • Thermal deviation • Resource fragmentation Step 4: Select a solver. Use simulated annealing or related heuristics to explore the solution space efficiently. Step 5: Iterate with real telemetry. Feed in live data: • DCIM • BMS • Scheduler metrics: Continuously refine the model. What “good” looks like • 10–25% improvement in GPU utilization • Lower east-west congestion without network upgrades • Reduced thermal excursions • Faster schedule generation cycles Where most teams fail • Overfitting the model before validating its impact • Ignoring real-time telemetry • Treating this as a one-time optimization instead of a continuous system Bottom line: You don’t need quantum hardware to get quantum-level thinking. You need a structured optimization model and the discipline to iterate it against real operating data. If you’re running >10MW environments and not doing this, you’re leaving efficiency and margin on the table. #DataCenters #AIInfrastructure #GPU #Optimization #HighPerformanceComputing #Cloud #Infrastructure #DigitalTransformation

Sr. Analyst @ Wayfair | MBA Candidate | Retail Logistics & Analytics | Exploring AI & Sustainability in Supply Chains

I spent the last few weeks building a logistics optimization model, using real US East Coast routes, real trade-offs between cost, load utilization, and carbon emissions. The model kept asking a question analytics alone can't answer: What happens next week? What if demand shifts? What if that carrier drops capacity again? That's where AI changes things, not by replacing judgment, but by making it faster and better informed. Three dimensions where I think the opportunity is real: 🛣️ Route optimization Most routing decisions are calculated once using cheapest path & fastest lane. AI makes routing continuously learning, balancing cost, delivery reliability, and emissions simultaneously across carrier availability, lane performance, and real-time conditions. In my own modeling, optimizing across mode and load variables drove a +19.4pp improvement in load utilization, a gain invisible when optimizing one variable at a time. Built using linear multi-objective optimization and scenario modeling across 28 route-mode combinations, with EPA SmartWay emission factors and SASB TR-RO metrics as the analytical foundation. 📈 Demand forecasting Logistics suffers when demand signals arrive too late, or carriers get booked reactively or routes get improvised. AI-driven forecasting changes the input, not just the output, generating probabilistic scenarios across seasons, regions, and SKU patterns rather than a single number. The goal: a forecast that updates fast enough to shift what you plan and route before the disruption hits. 🟢 Sustainability metrics Most teams track emissions once a quarter for an ESG slide. AI can make sustainability a real-time decision input. Using EPA SmartWay emission factors across truck, rail, and EV scenarios, my prototype showed 85–90% emissions reduction potential simply by reconsidering mode and load choices. AI operationalizes this at scale, embedding CO₂ per ton-mile into the routing decision itself, not as a constraint layered on top, but as an optimization target alongside cost and speed. That's the shift from sustainability as a metric to sustainability as a lever. I will be honest; I was cautious about AI for a while. In logistics, there's a lot of noise: tools that overpromise, implementations that ignore operational reality, dashboards that look impressive but don't connect to decisions. But working closer to the data changed my view. When AI is built on top of clean, connected analytics, the results feel different. Less like automation, more like augmentation. That shift, from analytics foundation to AI-powered decisions, is what I want to keep exploring. If you are working on AI applications in logistics or supply chain, especially where sustainability is part of the equation, I would genuinely love to connect.

Director @ Ember | Explaining Europe’s power sector with data | Energy & AI | enersite.app

There's 1 spot for a 100 MW data center in Belgium. Add 5% demand flexibility: suddenly there are 16. That’s the power of flexibility as visualized by Elia Group - Belgium’s transmission system operator. Looking at Elia’s grid capacity map for 2027, there’s literally one (new) spot in the country where you can connect a 100 MW data center. And 100 MW isn’t even that big. But if you allow for 5% of demand flexibility - which could mean shifting or reducing the load, or replacing it with on-site generation, there’s suddenly 16 free spots for new industrial loads, many up to 300 MW. If you go to 20% (which is a bit extreme), the grid pretty much stops becoming a bottleneck. Of course you wouldn’t want to close your data center for 2 months in a year. But actually, the yearly average load of data centers is 50%. So there’s clearly space for optimization. In fact, a recent trial by Nebius, Emerald AI, EPRI and National Grid showed that a test AI cluster in London could slash load by 30% in 40 seconds in response to sudden grid stress, while keeping critical jobs. Even better, it could sustain multi-hour load reductions of 10-40% and still deliver 99% performance on highest priority jobs. And you can optimize further: with onsite battery storage, you can shape the daily load profile to match the grid’s needs, potentially getting an accelerated connection agreement and saving money in the process. More in tomorrow's newsletter on data center flexibility. EDIT: a good point raised in the comments - I should clarify that the grid capacity map shows the connection capabilities for new projects, so on top projects that have already secured a grid connection.

Senior Data Engineer| Microsoft certified Azure Data Engineer| Certified Databricks Data Engineer Associate| Databricks| Python| SQL| PySpark| Spark SQL| Shell Scripting|hadoop|hive|apache spark

Apache Spark Optimization Techniques for Data Engineers Optimizing Apache Spark jobs is crucial for high-performance big data processing. Here are some key techniques to enhance efficiency: 🔹 Broadcast Joins – Use broadcast() for small datasets to avoid expensive shuffles. 🔹 Caching & Persistence – Cache frequently used DataFrames to reduce recomputation. 🔹 Efficient File Formats & Partitioning – Use Parquet and partition data for faster queries. 🔹 Avoid Data Skew – Distribute data evenly using salting to prevent performance bottlenecks. 🔹 Reduce Shuffle with Coalesce & Repartition – Optimize partitioning to balance workload. 🔹 Enable Adaptive Query Execution (AQE) – Let Spark auto-optimize partitions & joins. 🔹 Use Pandas UDFs – Replace standard UDFs with vectorized Pandas UDFs for better performance. Implementing these techniques can boost performance by up to 80%!

Electrical Engineer | Data Center Specialist with Expertise in Power Distribution and Efficiency Optimization

Optimizing Data Center Calculations: A Professional Approach to Efficient Design and Operation 1. Total Power Consumption & Load Calculations A comprehensive power budget begins by aggregating the rated power of all IT equipment. Additional loads—such as cooling systems, lighting, and redundancy overhead—must be incorporated Total Power = (Sum of IT Load) * Redundancy Factor + Cooling Load + Auxiliary Loads Reference: For detailed methodologies, refer to IEEE Standard 1100-2005 [1] and IEC 60364 [2]. 2. UPS and Backup Sizing To guarantee uninterrupted operations, the sizing of UPS systems should account for both the total load and the power factor: UPS Capacity (kVA) = (Total Power Requirement (kW) / Power Factor) * Contingency Factor Reference: IEEE guidelines on power quality and UPS systems (see IEEE Recommended Practice for Powering and Grounding of Electronic Equipment [1]). 3. Voltage Drop Analysis Ensuring that voltage levels remain within acceptable limits throughout the facility is critical. Calculate voltage drop using: Voltage Drop (V_drop) = Current (I) * Conductor Resistance (R) This helps in selecting appropriate cable sizes and materials while ensuring compliance with IEC standards. Reference: Detailed procedures and examples can be found in IEC 60364 and associated national annexes [2]. 4. Cooling Load & Thermal Management Data center cooling calculations involve determining the heat output from IT equipment and auxiliary systems. The Power Usage Effectiveness (PUE) metric is frequently used to evaluate the efficiency of cooling infrastructure: PUE = Total Facility Power / IT Equipment Power Achieving a lower PUE indicates more efficient energy use, highlighting the importance of optimizing both IT and facility systems. Reference: ASHRAE’s “Thermal Guidelines for Data Processing Environments” offer a comprehensive analysis of thermal load management. 5. Harmonic Distortion Considerations With the proliferation of non-linear loads, it is essential to assess harmonic distortion levels. Following IEEE 519 guidelines assists in designing filters and mitigating potential power quality issues. Reference: IEEE Standard 519 provides the necessary limits and mitigation strategies. Conclusion Integrating these calculations into the data center design process not only improves operational efficiency but also ensures adherence to globally recognized standards. References • IEEE Standard 1100-2005 – IEEE Recommended Practice for Powering and Grounding of Electronic Equipment. Available at: IEEE Xplore Digital Library • IEC 60364 Series – Low-Voltage Electrical Installations. Available at: IEC Webstore • ASHRAE Thermal Guidelines for Data Processing Environments. Available at: ASHRAE Website • IEEE Standard 519 – Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. Available at: IEEE Xplore Digital Library