In the modern energy sector, data is often trapped in operational silos—telemetry, emissions tracking, and grid consumption exist in isolation. This platform was engineered within Microsoft Fabric to unify these streams into a Single Source of Truth, enabling data-driven decisions across three critical pillars:
Operational Excellence: Optimizing plant load factors to reclaim 23.41M MWh of unused capacity.
ESG Leadership: Real-time tracking of carbon intensity, maintaining a fleet average of 0.05 kg/MWh against a 0.10 target.
Grid Resilience: Monitoring heat balance to sustain a 102.59% self-sufficiency rating and prevent system deficits.
By unifying these datasets, I identified critical operational risks that were previously buried in siloed telemetry.
Efficiency Risk Identified: 81,744 MWh of recorded excess waste across the fleet.
Estimated Financial Impact: Translated technical waste into a financial narrative representing ~€4,904,640 in avoidable costs.
Primary Risk Driver: Identified Nuclear production at the Tampere Plant as the leading contributor to efficiency risk.
Fleet Utilization: Maintained a stable but low capacity utilization of 13.2%, highlighting a massive opportunity for scaling without further CAPEX.
All code, including Spark notebooks, SQL scripts, and DAX measures, is version-controlled and fully documented.
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The district energy fleet generated a total of 3.56M MWh, reflecting a steady Month-over-Month (MoM) increase of +0.0606 MWh. While total output is rising, the fleet is currently operating with a massive 23.41M MWh of unused capacity and a low overall utilization rate of 13.20%.
The Tampere Benchmark: Tampere is the fleet's primary producer, contributing 2.0M MWh. It is the only plant operating significantly above the average energy line of 712,024 MWh and boasts the highest capacity utilization at 22.90%.
Renewable Transition: Rovaniemi (0.7M MWh) and Oulu (0.5M MWh) are the primary drivers of renewable energy within the mix. Rovaniemi, in particular, shows healthy growth with a +6.42% increase in utilization.
Operational Friction: The Espoo and Vaasa plants are currently underperforming, with utilization rates dropping by nearly 10% each. Their combined output is minimal, and they represent the highest levels of untapped potential in the fleet.
Reliability Risk: With Tampere producing more than double the average of all other plants combined, the operation has a "single point of failure" risk.
Efficiency Opportunity: The primary growth lever for this portfolio is not building new capacity, but rather reclaiming the 23.41M MWh of unused capacity currently sitting idle in the Southern plants (Vaasa and Espoo).
Decarbonization Note: While Tampere leads in volume, its output is classified as Non-Renewable, whereas the smaller Rovaniemi and Oulu plants lead the fleet's sustainability efforts.
A key finding in this report is the 23.41M MWh of unused capacity. While the Month-over-Month (MoM) increase of +0.3970 in idle capacity may initially appear as an operational inefficiency, it represents a significant strategic advantage:
Scalability Without Investment: This surplus highlights a massive opportunity to scale energy output to meet future demand without requiring further Capital Expenditure (CAPEX).
Efficiency Buffer: Leveraging this existing infrastructure allows for rapid operational expansion, turning idle assets into a primary growth lever for the portfolio.
This dashboard highlights a sophisticated balance between high-volume operational requirements and Environmental, Social, and Governance (ESG) goals:
Volume Leadership: The Tampere Plant serves as the fleet's "Volume Leader," providing the necessary scale with 2.0M MWh of output to ensure grid stability and reliability.
Sustainability Drivers: In contrast, the Rovaniemi and Oulu plants act as the "Sustainability Leaders," driving the fleet’s transition toward renewable energy sources.
Strategic Integration: By visualizing these together, the report demonstrates a holistic management approach—maintaining the scale provided by traditional sources (Non-Renewables) while aggressively monitoring and growing the renewable energy mix.
The fleet currently maintains a 43.59% Renewable Share of Energy, totaling 1.55M MWh of green power. This reflects a slight but positive Month-over-Month (MoM) increase of +0.0017%. The average carbon intensity remains exceptionally low at 0.05 kg/MWh, well below the strategic target of 0.10 kg/MWh.
The High-Efficiency Leader: The Tampere Plant is the fleet's "Star Plant," achieving the highest total energy output while maintaining a near-zero CO₂ intensity.
The Intensity Challenge: The Oulu Plant represents the primary sustainability challenge, with a CO₂ intensity of 0.24 kg/MWh. Despite having median production levels, it accounts for the highest total emissions at 163.52k kg CO₂ .
Low-Impact Operations: Espoo and Rovaniemi are characterized as low-intensity performers, contributing minimally to the fleet's total carbon footprint.
Target Achievement: The fleet is successfully operating at 50% of the maximum allowable CO₂ intensity target, signaling strong environmental compliance.
Decarbonization Priority: Future sustainability efforts should prioritize technological upgrades or fuel switching at the Oulu Plant, as it is the sole outlier exceeding the fleet's target intensity line.
Balanced Energy Mix: The portfolio currently relies on a 56.41% Non-Renewable base to ensure grid stability, but the steady growth in renewable volume (+0.0265 MoM) shows a consistent transition trend.
In this analysis, I utilized a quadrant-based scatter plot to categorize asset performance based on environmental impact versus productivity.
Target Objective: The ultimate goal for the fleet is to migrate all assets toward the bottom-right quadrant (High Output / Low CO₂ Intensity).
Tampere Plant as a Model: By identifying Tampere as the "Star Plant," we establish a benchmark for high-volume production that remains environmentally compliant, providing a blueprint for future retrofitting across the fleet.
The use of the CO₂ Intensity vs. Output visualization serves as a primary diagnostic tool for capital expenditure (CAPEX) planning:
Outlier Identification: The chart immediately flags Oulu Plant as a critical outlier. Despite moderate production, its position in the upper-left quadrant indicates a disproportionate carbon footprint.
Executive Decision Support: This visualization enables management to prioritize investments. Instead of broad fleet-wide changes, resources can be surgically directed toward high-intensity outliers to achieve the greatest reduction in total emissions for the lowest cost.
The district energy fleet is operating at a 102.59% Heat Self-Sufficiency rating, confirming that internal production fully covers consumer demand. The system currently maintains a zero-MWh heat deficit, with reliability improving significantly through a net increase of +150 surplus days this month.
The Reliability Leader: Consistent with its energy output performance, the Tampere Plant is the leading contributor to grid stability, maintaining the highest individual heat surplus at 17.99K MWh.
Uniform Performance: Unlike the energy production metrics, heat balance is well-distributed across the fleet, with all five plants—including those with low energy utilization like Espoo and Vaasa—contributing surpluses between 16.93K and 17.35K MWh.
Seasonal Resilience: Heat production consistently tracked above consumption throughout the year, peaking in August with a total heat volume of 0.56M MWh.
Risk Mitigation: The Heat Surplus/Deficit Trend remains consistently above the Safety Threshold of 0, indicating a robust buffer against unexpected spikes in demand or plant downtime.
Fleet Interdependence: While energy production is highly centralized at Tampere, heat reliability is a shared fleet strength, suggesting that heat infrastructure is more evenly optimized across all geographic locations.
In this analysis, I focused on the Safety Threshold as the primary indicator of operational health.
Beyond 100%: Achieving over 100% self-sufficiency isn't just about meeting demand; it's about the ability to export or store energy, creating a secondary revenue stream or a community safety net.
Predictive Reliability: By tracking "Surplus Days," we transition from reactive reporting to predictive maintenance, ensuring the fleet has enough "fuel in the tank" to handle seasonal volatility without external help.
The Production vs. Consumption bar chart was specifically designed to highlight the "Delta." By showing that production (Blue) always sits slightly higher than consumption (Pink), we provide immediate visual confirmation of the fleet's reliability to stakeholders without requiring them to read the raw data.
The fleet is currently navigating a complex efficiency landscape with a total Efficiency Risk of 81,744 MWh recorded across the dataset. This level of inefficiency translates to approximately €4,904,640 in avoidable costs, highlighting a critical opportunity for bottom-line financial recovery through technical optimization.
The Primary Risk Driver: The Tampere Plant (Nuclear) represents the single highest individual efficiency risk at 16,987 MWh. While it is the volume leader, its scale also makes it the most susceptible to high-value waste.
Resource-Specific Risks: Efficiency risks are distributed across all energy types, with Solar (16,349 MWh) and Wind (16,344 MWh) showing significant volatility. Biomass at the Oulu plant currently carries the lowest relative risk at 15,929 MWh.
Utilization vs. Emission Trade-off: There is a stark contrast between high-utilization/low-intensity plants (Tampere and Rovaniemi) and low-utilization/high-intensity plants (Oulu). Oulu remains a double risk, combining low capacity utilization (8.33%) with high carbon intensity.
The "Max Waste" Threshold: The system consistently exceeds the 5,000 MWh Max Allowable Waste line, with efficiency risks peaking in August (2.6K MWh).
Utilization Stability: Despite high waste figures, capacity utilization remains remarkably stable, hovering between 13.18% and 13.22% throughout the fiscal year.
In this analysis, I translated technical waste (MWh) into a financial metric (€).
Why this matters: Executives may not always prioritize "MWh waste," but they always prioritize €4.9M in avoidable costs. This demonstrate my ability to bridge the gap between engineering data and corporate finance.
The use of the trade-off scatter plot is designed to identify the "Sweet Spot" of operations:
The Goal: High Capacity Utilization + Low CO₂ Intensity.
The Insight: By mapping these two metrics together, I've shown that low utilization is often a leading indicator for high emission intensity (as seen with Oulu), suggesting that "running lean" is not just a financial risk, but an environmental one.
The "Operational Efficiency & Waste Trend" chart includes a Dynamic Risk (Blue Bar) overlaying the Capacity Utilization (Orange Line).
Design Choice: This was built to monitor if increases in utilization lead to higher waste. The data shows they are currently decoupled, meaning the waste is likely due to process inefficiencies rather than "overworking" the plants.
I implemented a code-first engineering approach using Apache Spark to handle high-volume energy datasets. By moving business logic upstream into the Lakehouse, I ensured that data is "Clean, Curated, and Consistent" before it reaches the executive layer.
Bronze (Raw): Ingestion of multi-source energy data (CSVs and APIs) into OneLake.
Silver (Validated): Data cleaning, deduplication, and schema enforcement using Fabric Notebooks (PySpark).
Gold (Curated): Star-schema optimized Delta tables pre-calculated for high-speed analytics.
Logic Upstream Strategy: Unlike traditional deployments that rely on heavy DAX, I moved complexity into Spark. Pre-computing KPIs like Capacity Utilization and Carbon Intensity ensures that "Renewable %" remains identical whether viewed in Power BI, SQL, or Python.
Data Flow Architecture
High-Level Architecture Diagram
Spark notebooks and SQL scripts
The final output is an executive-grade suite of dashboards designed to translate technical metrics into financial and environmental strategy.
Focus: Asset Scaling & CAPEX Optimization.
Strategic Insight: Identified Tampere Plant as the primary volume driver (2.0M MWh) while highlighting a +0.3970 MoM increase in unused capacity—representing a massive opportunity for scaling without new capital expenditure.
Focus: Decarbonization and ESG Compliance.
Strategic Insight: Utilized a CO₂ Intensity vs. Output scatter plot to identify Oulu Plant as an intensity outlier (0.24 kg/MWh), enabling surgical CAPEX intervention rather than inefficient fleet-wide upgrades.
Focus: Grid Stability and "Safety Margin" Monitoring.
Strategic Insight: Confirmed a zero-MWh heat deficit across the fleet. By tracking "Surplus Days," the platform transitions from reactive reporting to predictive reliability, maintaining a consistent buffer above the safety threshold.
Focus: Financial Recovery and Waste Mitigation.
Strategic Insight: Translated technical waste into a financial narrative, identifying €4,904,640 in avoidable costs. This page bridges the gap between engineering "MWh" and corporate "€" to prioritize the 81,744 MWh of recorded efficiency risk.
CI/CD: Full Git integration with deployment pipelines (Dev/Test/Prod).
Security: Row-Level Security (RLS) ensuring sensitive plant-specific data is restricted to authorized personnel.
Connectivity: Utilized DirectLake for instant visibility into load changes without the latency of scheduled refreshes.
Governance & DevOps Architecture Overview