Minute-by-minute home energy datasets are no longer a luxury — they're the foundation of accurate forecasting, effective demand response, and a grid that can absorb the solar and EV revolution.
Utilities have historically relied on monthly meter reads or, at best, hourly interval data. The modern grid demands far more.
Rooftop solar arrays feed surplus energy back onto local feeders during midday. Behind-the-meter batteries charge and discharge on price signals. Dozens of EVs plug in at 6 PM just as solar collapses. Without a time-synchronized, home-level view of these loads, operators risk under-forecasting peaks, overbuilding infrastructure, and missing megawatts of latent flexibility.
The electricity system has fundamentally shifted. Homes are no longer monolithic, opaque loads — they're collections of behavioral patterns capable of delivering megawatts of grid flexibility.
Central generation plants ramped output to meet load peaks. Monthly billing totals drove planning. Forecasting errors were absorbed by large contingency reserves — a luxury the modern grid no longer enjoys.
Distributed solar slashes midday net loads to near zero, only for the evening to spike as air conditioners switch back on and EVs begin charging — all within a 30-minute window. Fifteen-minute meter reads can't capture a compressor's 5–10 minute cycle.
One-minute resolution data identifies exactly when a thermostat ramps the compressor, separates that from a pool pump draw, and isolates an EV charging event. Each home becomes an intelligent, flexible node.
Over three years of one-minute data from 10,000+ Arizona homes, combined with load-level disaggregation and metadata on occupancy and equipment age — enabling forecasting and programs that were simply impossible before.
Each additional layer of granularity unlocks a new tier of operational and economic value — from more accurate forecasts to deferred infrastructure spend worth millions.
Incorporating one-minute disaggregated HVAC data into neural networks reduces next-day peak load forecast errors by 15–20% for regional utilities. A 2% error on a 1 GW system can cost tens of thousands in unnecessary generator dispatch alone.
Precise per-home load signatures let utilities cluster similar homes and construct dispatch signals guaranteeing a target MW reduction — delivering far greater reliability than behavioral alerts that homeowners may simply ignore.
Minute-by-minute data reveals when each home's water heater or pool pump can run without discomfort, enabling SEMS to absorb surplus solar instead of curtailing it — increasing local solar utilization by 15–20%.
Actual disaggregated load profiles reveal which feeders are near thermal limits and which homes have behind-the-meter flexibility — replacing conservative worst-case sizing with data-driven precision that saves ratepayers millions.
Accuracy improved from initial 85% baseline through continuous model retraining.
From machine learning research to low-income housing programs, Inergy's high-resolution dataset has demonstrated consistent, quantifiable results across every application context.
Low-income households often suffer the highest energy burdens yet are least likely to participate in demand response. Inergy partnered with local housing authorities to deploy controllers in a 200-unit affordable housing complex in Phoenix.
85% of residents reported feeling more empowered understanding exactly how their usage translated into cost savings — an early indicator that data transparency fosters broader energy-smart behaviors.
Graduate students at a California university used Inergy's dataset to train a convolutional neural network for non-intrusive load monitoring (NILM).
Capturing one-minute resolution data at scale requires specialized hardware, robust cloud infrastructure, and a privacy framework built from the ground up.
Energy usage patterns reveal personal habits — when people wake up, leave for work, or return home. Inergy treats privacy as foundational, not an afterthought.
Each residence is assigned a randomized identifier. Location data is aggregated to census tract level — never disclosed at address level.
Only authorized Inergy personnel and vetted research partners can access raw time-series data. Third-party analysis runs against anonymized copies only.
Adheres to FERC data security guidelines, state PUC requirements, and CCPA. Quarterly penetration testing and annual third-party audits validate ongoing security posture.
Processing 5 billion data points per year across 10,000 homes — with sub-10-second dispatch latency for ancillary markets — requires solving hard infrastructure problems.
Cloud-native time-series database optimized for write-heavy workloads, partitioned across multiple nodes and sharded by home ID. Additional storage clusters spin up automatically as the network grows. Ingestion latency held under 30 seconds at any scale.
Deep-learning CNNs analyze temporal waveforms at second-level resolution, learning subtle compressor ramp-up curves versus step-function EV charger draws. Continuous model retraining against a 5% ground-truth submeter subset improved accuracy from 85% to 95%+.
SEMS issues dispatch instructions via secure MQTT over low-latency cellular or Wi-Fi mesh, verifying curtailment within 5–10 seconds — enabling ancillary and frequency regulation markets. OpenADR 2.0 over AMI backhaul serves less time-sensitive peak shaving programs.
For utilities, regulators, and researchers ready to act — a practical framework for harnessing residential energy data at scale.
Performance-based rate mechanisms can reward utilities that achieve peak reductions using minute-level data. Work with regulators to modernize M&V protocols to accept one-minute baselines rather than hourly estimates.
Start with "data champion" homes — existing smart thermostats or high solar penetration — to anchor ground-truth. Expand to diverse geographies in phase two. Scale to full service territory only after accuracy and reliability are proven.
Show participants exactly how their curtailment — say, shaving 1.4 kW during a 4 PM event — translated into a $3.60 bill credit. Inergy's portal drives participation rates exceeding 90%. Clear opt-out options are non-negotiable.
Anonymize all data streams before partner access. Aggregate location to census tract level. Disclose privacy policies in advance and secure explicit customer consent. Periodic third-party audits reinforce trust and CCPA compliance.
Access the full white paper including detailed pilot methodologies, technical architecture specs, privacy framework documentation, and recommendations for utilities, policymakers, and researchers.