PV Predict smoothing the wrinkles in the distributed generation electrical grid
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About Us

PVPredict develops machine learning algorithms for advanced statistical performance monitoring and PV generation forecasting. Six years in development with support from the Israeli ministry of energy has led to a SaaS product now available to monitoring companies to enable their customers to effectively ascertain the health of their systems every day while enabling grid managers to reclaim control of the grid flow.

Target customers:

  • Energy traders with PV in their portfolio
  • Transmission system operators
  • Distribution system operators
  • PV monitoring companies
  • PV fleet owners
  • Inverter manufacturers

The Challenge

Over half of the installed PV power in most countries is installed on rooftops and managed by residential or commercial customers. These systems lack good performance monitoring due to the cost of hardware and software. These customers are not aware of their system’s availability beyond the receipt of their monthly electrical bill. Yet they aggregate into large virtual PV power plants that feed the national grid as an unknown variable to the grid manager. So the system owner has no idea how his system is performing from an objective point of view, if at all and the entities managing the grid at both the national and local level are dealing with rogue power plants they cannot take into account in the delicate undertaking of electrical grid management.

The Solution

A Statistical Performance Monitoring System that requires:

  • No hardware
  • No input of system configuration
  • No analysis software

Requiring only: Inverter or generation meter output & GIS location Benefactors of our system include:

  • Owners of small, medium even large PV systems
  • PV monitoring companies
  • PV fleet owners
  • Inverter manufacturers
  • Energy traders including PV in their portfolios
  • Transmission System Operators (TSO)
  • Distribution System Operators (DSO)

Services

PVPredict has developed machine learning algorithms that predict the next day’s hourly generation of any solar PV inverter without the use of sensors or knowledge of the system configuration. The algorithms also show promise in predicting failures before power loss occurs. These algorithms are the core technology for the following services:

  • PV System Performance Monitoring
  • Day ahead and Hour ahead energy trading
  • Energy forecasting for Utility reporting and reducing spinning reserve
  • Aggregation for Virtual Power Plant (VPP)
  • Validation for green certificate programs
  • Distributed Energy Resource Management System (DERMS)

Our Team

Mike Green
Founder
Electrical engineer; Owner of M.G. Lightning ltd. design and consulting; former CTO of Arava Power; consultant to the Israel Standards Institute on rooftop solar; represents Israel in the IEA-PVPS Task 13 researching the efficiency and reliability of PV systems

Eyal Brill Ph.D
Founder
Expert on unsupervised machine learning; owner of Decision Makers ltd.; post-doctoral degree from the University of Maryland; Deputy Head of the MOT faculty at the Holon Institute of Technology in Israel

Shimshon Rapaport
Electrical Engineer
PV systems engineer developing PV predict via hardware/software integration, data analysis, web development and database management. 

Adam Hirsch Ph.D
Business Development
Former NREL researcher with energy efficiency and smart grid experience. Lecturer in Energy Systems and Sustainability at the Herzliya Interdisciplinary Center. Bachelor’s (Harvard University) and Ph.D. (University of California at Irvine) degrees in the Geosciences.

Adi Brill
Software Developer

Michael Britvin
Data Integrator

Barak Brill
Software Developer

Strategic Partners

InfoPV is the home of the SolarEra.net backed program for our Distributed Energy Resource Management System (DERMS) pilot project designed to enable a DSO to manage the voltage in residential distribution grids with a high level of solar PV penetration while aggregating the neighborhood into a virtual PV plant for reducing spinning reserve.

Israel’s Ministry of Energy is a long time supporter and has provided three rounds of investment in PVpredict.

A member of the IEA PVPS-Task 13 since 2010, we have collaborated with PV experts from over 25 countries. our development can be found described in the 2017 report “Improving Efficiency of PV Systems Using Statistical Performance Monitoring”.

DecisionMakers ltd. is the company owned and managed by Dr. Eyal Brill in which the machine learning algorithms driving PVPredict where developed.

Owned and managed by Mike Green is the vehicle that drove the first 5 years of Pvpredict development opposite the world solar PV market.

Greeneum incentivizes and optimizes renewable energy trading using blockchain and personalized AI

Timeline

January 2013

Received Israeli Ministry of Energy “Heznek” grant to develop machine learning algorithms for predicting next day’s hourly PV generation without the use of sensors

April 2014

Our algorithms proved more accurate than those used by commercial companies in Europe for predicting next day’s hourly generation for selling on “Next Day Market”

December 2014

Successful conclusion of Min. of Energy development grant, begin preparing marketing foundation for selling SaaS to residential customers as a statistical performance monitoring solution requiring no sensors or input from the owner

January 2015

Received 2nd Min. of Energy “Heznek” grant to develop machine learning algorithms that will predict faults before they occur using clustering regression-tree statistical methodology aided by expert input

September 2015

Received Min. of Economics (Innovation) grant for joint development with University of Cyprus of algorithms for the early and reliable detection of degradation in Photovoltaics (PID)

March 2016

Our server platform is capable of delivering “State of Health” mail to PV owners each morning as well as warning immediately on loss of power and loss of communication

December 2016

Successful conclusion of our “Early fault warning” development funded by the Israeli Ministry of Energy

January 2017

Received the SOLAR-ERA.NET project as consortium leader in a pilot project in Cyprus and a Kibbutz in Israel to apply our algorithms for managing the distribution of residential PV energy in an LV distribution grid Utility market

June 2018

Successful conclusion of our “Early fault warning” development funded by the Israeli Ministry of Energy

December 2018

Completed advanced algorithms for use with hour ahead and 5 minute ahead energy forecasting in Virtual Power Plants, energy trading and for continuing the DERMS research

January 2019

Pilot with FSIGHT at Maaleh Gilboa DERMS project supplying residential PV predictions

March 2019

Pilot project with Emulsionen, a Swedish monitoring company interested in offering their Prosumers the PVpredict performance monitoring capability

July 2019

Pilot with Kyocera to enable predictions in Virtual Power Plants

September 2019

Pilot with Repom, Romanian based renewable energy asset manager for supplying day ahead predictions for utility grade dispatched PV power stations

Contact Us

Email: mike[at]lightning.co.il
Ph: +972 54-499-9169
Address:11 Hasadna Street Suite 101 Raanana, Israel

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