Grid4C – Cutting Edge Smart Grid Predictive Analytics

Grid4C empowers all Energy value chain participants by providing them the power to foresee, leveraging advanced Machine Learning capabilities to deliver accurate, granular predictions, which are crucial for tackling the rising challenges of today’s Energy Industry. Our solutions enable to analyze the massive amount of sub-hourly data collected from millions of smart meters, together with customer data, pricing information and more, in order to maximize the efficiency of Energy operations and increase customer engagement.

The Founder 

Dr. Noa Ruschin-Rimini has more than 15 years of experience in the software industry. She held several positions in leading software companies, including Head of the Presales Department at Oracle, Sales Manager at IBM, and Business Development Director in several Start-Up companies. Noa holds a PhD in Machine Learning and Data Mining from the Engineering Faculty at Tel Aviv University, specializing in Anomaly Detection and Predictive Analytics of Time Series. She has published papers for prestigious conferences and journals in the field of Machine-Learning, and held a lecturer position. Her other areas of expertise include ERP, CRM, PLM and BI systems.

Technology
The Grid4C engine monitors each meter separately, automatically learns its underlying correlations, and uses unique information-theory based algorithms in order to decompose each meter’s behavior into sub-series, which are then automatically modeled. The exclusive ‘problem decomposition’ feature visually presents the meters ‘personalized behavior rules’ in a way that can be easily understood by both energy consumers and energy providers. These rules enable to extract real-time actionable insights, perform various types of ‘what if’ analysis, in a manner that is intuitive and intelligible to the end user. After the modeling phase, the Grid4C engine monitors each meter,

using distinctive anomaly detection algorithms, in order to detect early warnings of changes in the meters’ patterns and behavior. Whenever such a deviation is detected, the Grid4C solution automatically identifies the irregularities’ signature, and determines whether irregularities are caused due to bypass and tampering, meter malfunctioning, or change in consumption patterns. Whenever changes in consumption behavior are identified, changes are classified and real-time actionable insights are extracted. The result is a full self-learning adaptive mechanism, guaranteeing accurate predictions, detection of anomalies at an early stage, and real-time actionable insights.

Solutions

The Grid4C Predictive Analytics SaaS solutions are based on proprietary Machine Learning Big-Data algorithms, guaranteeing real time accurate and reliable predictions, in a fully automated, plug-and-play manner. The data and predictions are presented with advanced visual tools, enabling end users to self-explore, gain insights and comprehend the data, without requiring any statistical background. The company provides Software as a Service (SaaS) analytics offerings aimed at all parties participating in the energy value chain: utilities, traders, distributers, grid operators, renewable energy power stations, electricity retailers and energy consumers.

The Grid4C portfolio consists of the following applications:

• Grid4C Load Research and Forecasting
• Grid4C Meters Operational Monitoring
• Grid4C Revenue Protection
• Grid4C Solar Power Forecasting
• Grid4C Customer Segmentation & Targeting
• Grid4C Customer Cross Sales Analysis
• Grid4C Customer Churn Analysis
• Grid4C Customer Engagement