machine learning and AI-driven planning & control

automatically learning, adapting and automating energy optimization tailored to your specific site, local conditions and energy usage all the time

local renewables is an increasingly complicated environment beyond simple human management

  • inter-dependent local characteristics

    each location, premise, site and system configuration has its own inter-dependent set of characteristics affecting ‘real-world’ performance/yield of renewables

  • different demand profiles by environment

    energy demand patterns in residential, commercial, industrial, agricultural and other applications will vary and need to be managed differently

  • 'one-size' fits all approach will under-perform

    a ‘one-size fits all’ approach to system design and management will never meet a users particular goals nor achieve optimized financial returns

  • constant balancing of supply & demand

    renewables are an intermittent resource and, in case of a shortfall in locally generated or stored energy, another source will need to be used to keep the lights on

  • grid time-of-use (TOUT) opportunities

    increasingly grids offer periods of ‘higher rate’ and ‘lower rate’ charges based on time-of-use, presenting threats & opportunities for grid rate exposure

sophisticated and proprietary algorithms use site specific data and weather-forecasts to optimize local energy use and efficient use of battery resources to reduce ‘waste’ in the form of excessive grid quantity or purchase of grid energy at higher rate times

the following compares different weather scenarios for a typical residential demand pattern showing how predictive smart management changes the quantity of grid taken in advance at lowest rate time depending on predicted future conditions aligned with the AI-driven automated daily plan

renewables systems without predictive management have no insight into future conditions and rely on preset grid charging setpoints which can only ever be optimal if the prevailing conditions at any point in time align with those settings

with a set-point preset at 80% and a minimum SOC of 20% this leaves

60% available for optimization !

system management strategy

“grid rate time of use tariff (TOUT) optimization”

hitting the optimized sweetspot of value with variable weather-conditions is impossible with any degree of consistency and unnecessarily increases grid costs

actively building partnerships with selected leading suppliers and systems integrators

who share our vision and seek rapidly deployable patented technologies for smarter local renewables which provide immediate additional value for end-user and drive profitable new services

CONDUCTIFY - live a more 'grid less life' with more independence and more control over your energy and other local resource goals