The Evidence Behind
SenseCore AI
Peer-reviewed research, compliance guides and sector insights — everything you need to understand how AI is transforming UK energy management.
Temporal Fusion Transformer for Half-Hourly Building Energy Forecasting
A TFT model trained on the BDG2 dataset achieves 10.31% MAPE across 30 commercial buildings — outperforming persistence, ARIMA and LSTM baselines. Built on MHHS half-hourly architecture.
Fuel Poverty Risk Scoring for UK Social Housing Portfolios
A novel scoring algorithm combining consumption patterns, occupancy inference and building characteristics to identify fuel-poor tenants before crisis. Mean score 23.8 (SD 20.0) across 785 residential buildings.
Multi-Method Anomaly Detection in Smart Building Energy Systems
LSTM Autoencoder, Isolation Forest and Z-Score methods applied to half-hourly smart meter data. Comparative evaluation across fault types, meter theft and occupancy-shift anomalies.
Gaussian Mixture Model Occupancy Inference Without Sensor Hardware
GMM-based occupancy estimation from electricity consumption patterns alone — eliminating sensor installation cost while maintaining inference accuracy for scheduling and HVAC optimisation.
Automated SECR Scope 2 Carbon Reporting from Smart Meter Data
A pipeline converting half-hourly consumption data directly into SECR-compliant Scope 2 carbon reports — removing manual calculation and audit risk from the compliance workflow.
SECR vs ESOS vs ERIC: Which Applies to Your Organisation?
A plain-English breakdown of who must comply with each framework, when, and what the penalties are — with guidance on how SenseCore AI automates each report.
SHDF Round 3: What Housing Associations Need to Know
The Social Housing Decarbonisation Fund requires consumption data and a fuel poverty risk assessment. This guide explains how SenseCore AI generates both from a single API call.
Why NHS Trusts Are Overpaying on Energy: The Data Gap Problem
NHS England spends £2.8B per year on energy. Without half-hourly forecasting and automated ERIC reporting, most trusts rely on manual spreadsheets and estimated baselines.
Fuel Poverty in Social Housing: From Problem to Policy
With 4.4 million social homes in the UK and rising energy prices, housing associations face regulatory pressure to identify and act on fuel poverty risk. This insight explains the FPRS approach.
API Reference: Getting Your First Forecast in 5 Minutes
A step-by-step walkthrough of authenticating against the SenseCore API, uploading half-hourly CSV data, and retrieving a 48-period ahead electricity forecast with SHAP attribution scores.
Understanding MHHS: The Architecture Behind SenseCore AI
Mandatory Half-Hourly Settlement is transforming UK energy data infrastructure. This guide explains how SenseCore AI is built on MHHS architecture — and why it future-proofs your platform.
Put the research to work.
The same AI behind these papers is live in production. See it forecast your building's consumption in under 5 minutes.