Whitepapers
WHITEPAPER
A Privacy-First Blueprint for
Secure Energy Innovation: EcoMetricx in Action
Andrei Ionete and Alex Ledbetter
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As the energy sector embraces digital transformation, safeguarding sensitive data is critical. This whitepaper outlines EcoMetricx’s zero-trust, privacy-by-design security framework, built to protect energy data while accelerating collaboration across utilities, aggregators, and service providers. Mapped to NIST CSF 2.0, CSA STAR CCM v4, and key regulatory mandates, the approach delivers continuous compliance, rapid partner onboarding, and resilient operations. EcoMetricx empowers stakeholders to innovate securely and meet today’s evolving cybersecurity and privacy demands.
WHITEPAPER
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations
Benjamin Westrich, MSc
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Advanced Metering Infrastructure (AMI) data from smart electric and gas meters enables valuable insights for utilities and consumers, but also raises significant privacy concerns. In California, regulatory decisions (CPUC D.11-07-056 and D.11-08-045) mandate strict privacy protections for customer energy usage data, guided by the Fair Information Practice Principles (FIPPs). We comprehensively explore solutions drawn from data anonymization, privacy-preserving machine learning (differential privacy and federated learning), synthetic data generation, and cryptographic techniques (secure multiparty computation, homomorphic encryption). This allows advanced analytics, including machine learning models, statistical and econometric analysis on energy consumption data, to be performed without compromising individual privacy.

We evaluate each technique’s theoretical foundations, effectiveness, and trade-offs in the context of utility data analytics, and we propose an integrated architecture that combines these methods to meet real-world needs. The proposed hybrid architecture is designed to ensure compliance with California’s privacy rules and FIPPs while enabling useful analytics, from forecasting and personalized insights to academic research and econometrics, while strictly protecting individual privacy. Mathematical definitions and derivations are provided where appropriate to demonstrate privacy guarantees and utility implications rigorously. We include comparative evaluations of the techniques, an architecture diagram, and flowcharts to illustrate how they work together in practice. The result is a blueprint for utility data scientists and engineers to implement privacy-by-design in AMI data handling, supporting both data-driven innovation and strict regulatory compliance.
WHITEPAPER
Unlocking Artificial Intelligence for California's Community Choice Aggregators
Matthew Harding, PhD.
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California’s Community Choice Aggregators (CCAs) stand at a pivotal moment. Serving more than fourteen million residents and managing more than sixty terawatt‑hours of annual demand, they have matured from local initiatives into market‑shaping institutions. Simultaneously, cloud‑based artificial‑intelligence (AI) platforms have become both affordable and exceptionally accurate, offering utilities worldwide new tools to strengthen grid reliability, deepen customer engagement, and reduce operating costs. This white paper explains why the convergence of these developments makes 2025 an optimal time for CCAs to embrace AI. It outlines the most relevant use cases, details pragmatic implementation steps, and highlights the governance measures needed to align technology deployment with public‑service values.
WHITEPAPER
Measurement & Verification for Behavioral Programs: Evaluating Programs That Have Gone Full-Scale
Matthew Harding, PhD.
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The evaluation of behavioral programs requires rigorous measurement and verification. While the randomized controlled trial (RCT) lies at the core of modern program evaluation, in most situations it is not feasible to implement a randomized approach. This is particularly true when programs have gone full-scale and the success of the program needs to be evaluated outside of the confines of an experimental framework involving the random allocation of households to treatment and control groups. This report aims to describe methods for performing a scientifically sound program evaluation in situations where a properly randomized experiment is not possible.
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