Automations
This pillar covers clean energy workflows that model wind or solar output and decide when to dispatch, store, or trade energy based on weather and pricing conditions. Pages should illustrate how a custom workflow stack improves renewable asset economics, reduces curtailment, and integrates forecasting with battery and grid control systems.
This foundational page outlines a custom, end-to-end agentic workflow that ingests weather data, predicts solar/wind generation, optimizes battery dispatch, and executes market bids to maximize renewable asset revenue. It details the architecture for integrating forecasting models, storage control systems, and ISO/RTO APIs, showing how to reduce curtailment and improve portfolio economics through automated, coordinated decision-making.
This page details a custom workflow where specialized agents collaborate to forecast generation, model price scenarios, calculate optimal bids, and submit them to power markets like CAISO or PJM. It explains how this automation reduces manual bidding risk, captures higher value from volatile markets, and integrates with forecasting engines and trading systems for a production-ready architecture.
This page covers a custom workflow that continuously forecasts energy prices and grid needs to optimize battery charge/discharge cycles, maximizing arbitrage revenue while preserving asset lifespan. It details the reinforcement learning and control logic required, integration with BMS and market data, and the operational savings from automating complex, real-time storage decisions.
This page explains a custom agentic system that predicts grid congestion and renewable over-generation to proactively dispatch storage or adjust plant output, minimizing costly curtailment. It covers the architecture for integrating grid operator signals, forecasting models, and plant control systems, delivering measurable ROI through increased energy sales and reduced regulatory penalties.
This page details a custom workflow that ingests SCADA, vibration, and weather data to predict component failures in wind turbines, automatically generating work orders and parts requests. It explains the multi-agent architecture for anomaly detection, root cause analysis, and integration with CMMS systems, reducing unplanned downtime and optimizing O&M spend for wind farm operators.
This page outlines a custom automation workflow that models a corporation's load profile against renewable generation forecasts and market prices to optimize PPA structuring and offtake decisions. It covers the data ingestion, simulation agents, and financial modeling required to reduce energy costs and de-risk renewable procurement for commercial and industrial buyers.
This page describes a custom workflow where agents continuously simulate merchant revenue under thousands of price and generation scenarios to calculate portfolio risk metrics. It details the architecture for high-throughput simulation, integration with trading and forecasting systems, and the risk management benefits of automated, daily VaR reporting for asset owners and financiers.
This page explains a custom automation workflow that agents use to ingest geospatial, environmental, and grid data to score and rank potential renewable project sites for development. It covers the orchestration of GIS analysis, financial modeling, and report generation, significantly accelerating the site due diligence process and improving project pipeline quality.
This page details a custom workflow that automates the calculation and update of time-variable green energy rates based on real-time renewable generation mix and market costs. It explains the agentic architecture for ingesting generation data, applying pricing logic, and pushing updates to billing systems, helping utilities attract customers with competitive, transparent offerings.
This page covers a foundational data orchestration workflow where agents automatically collect, validate, and harmonize data from operational technology (OT) and IT systems across a renewable portfolio. It details the architecture for ensuring data quality, handling API failures, and creating a single source of truth, which is critical for downstream forecasting and trading automation.
This page describes a custom, high-frequency forecasting workflow that uses satellite imagery and ground sensor data to predict solar irradiance and wind speeds for the next 15-60 minutes. It details the computer vision and ML pipeline architecture, and how integration with storage dispatch systems enables rapid response to cloud cover or wind gusts, optimizing real-time balancing.
This page outlines a custom, low-latency workflow where agents monitor real-time energy prices, calculate battery dispatch signals, and execute trades to capture intraday arbitrage opportunities. It explains the system architecture for high-frequency data ingestion, decision logic with guardrails, and integration with grid APIs, directly linking automation to increased storage revenue.
This page details a custom orchestration workflow that aggregates forecasts from distributed solar, storage, and flexible load assets to form a VPP, then automatically calculates and issues dispatch signals to participants. It covers the multi-agent coordination, settlement logic, and integration with behind-the-meter systems required to monetize distributed resources at scale.
This page explains a custom workflow where agents predict transmission constraints using grid models and generation forecasts, then automatically trigger preventive actions like storage dispatch or plant derating. It details the integration with utility network management systems and the operational savings from avoiding congestion charges and reliability must-run payments.
This page covers a custom automation workflow that analyzes performance data and weather history to detect soiling losses on solar farms, then automatically schedules and dispatches cleaning crews. It details the computer vision and scheduling agent architecture, integration with O&M platforms, and the yield improvement and labor cost savings from optimized cleaning cycles.
This page outlines a custom workflow for commercial/industrial sites that automates the decision to consume grid power, discharge on-site storage, or curtail load based on real-time prices and solar generation. It details the edge-based control logic, integration with building management systems, and the measurable reduction in demand charges and overall energy spend.
This page describes a custom workflow where agents monitor REC inventory, market prices, and compliance deadlines to automatically execute trades and retire credits for regulatory or voluntary purposes. It explains the architecture for connecting to REC registries, pricing feeds, and settlement systems, reducing administrative overhead and ensuring compliance for asset owners and traders.
This page details a custom workflow that ingests decades of climate model data to simulate long-term energy production under various climate scenarios, automating a critical step in project financing. It covers the orchestration of climate datasets, physical plant models, and uncertainty analysis, providing lenders and investors with robust, automated P50/P90 yield assessments.
This page explains a custom workflow for sites with multiple storage technologies (e.g., lithium-ion, flow batteries) where agents coordinate dispatch based on each technology's performance characteristics and degradation curves. It details the optimization architecture and how it maximizes total fleet value by intelligently allocating cycles, a key concern for operators managing diverse storage assets.
This page covers a custom automation workflow where agents monitor grid voltage and automatically adjust the reactive power output of inverters at solar or wind plants to provide grid support services. It details the control loop architecture, integration with plant SCADA, and the new revenue stream it creates from ancillary service markets for renewable generators.
This page describes a custom diagnostic workflow where agents correlate SCADA alarms, weather data, and maintenance logs to automatically identify and rank the most likely causes of underperforming renewable assets. It explains the multi-step reasoning architecture and integration with ticketing systems, drastically reducing engineer troubleshooting time and improving fleet availability.
This page outlines a custom workflow for asset owners with plants in multiple ISOs, where agents optimize the allocation of energy and ancillary services across different markets to maximize portfolio revenue. It details the cross-market arbitrage logic, data harmonization challenges, and the significant upside from automating what is typically a manual, sub-optimized process.
This page details a custom workflow that automates the decision to run a green hydrogen electrolyzer, balancing electricity costs against hydrogen market prices and storage tank levels. It covers the integration of price forecasts, electrolyzer efficiency curves, and control systems, enabling profitable operation of flexible industrial load for renewable integration.
This page explains a custom workflow that automates the tracking of a project's status in the utility interconnection queue, ingests study reports, and extracts key cost and timeline data. It details the document processing and project management agent architecture, reducing the manual labor and delay risk associated with navigating complex interconnection processes.
This page covers a custom workflow that automates the allocation of solar generation credits to subscribers in a community solar program, handling monthly true-ups, billing adjustments, and subscriber onboarding/churn. It details the integration with metering, billing, and CRM systems, reducing operational costs for utilities and solar developers running shared asset programs.
This page describes a custom automation workflow where agents forecast a renewable plant's ability to provide frequency regulation or reserves, formulate bids, and respond to automatic generation control (AGC) signals. It details the low-latency architecture and stringent reliability requirements, unlocking a high-value revenue stream for assets with fast-responding inverters.
This page outlines a custom workflow that uses degradation models to evaluate the long-term cost of each charge/discharge cycle, integrating this with real-time price signals to make lifespan-aware dispatch decisions. It details the sophisticated cost-function optimization and how it protects asset value, a critical build for operators focused on total cost of ownership.
This page details a custom workflow that combines generation forecasts with publicly available transmission outage data to predict localized constraints days in advance. It explains how agents use this insight to recommend pre-curtailment or storage charging strategies, giving plant operators a proactive tool to avoid sudden, costly derates.
This page covers a custom workflow that automatically processes thermal and visual imagery from drone flights over solar farms, detects panel defects or vegetation encroachment, and routes confirmed issues to maintenance teams. It details the computer vision pipeline, GIS integration, and the dramatic reduction in manual inspection and analysis labor.
This page explains a custom workflow for sites with on-site solar and electric vehicle fleets, where agents schedule charging sessions to maximize solar self-consumption and minimize grid demand charges. It details the orchestration between solar forecasts, charger APIs, and fleet management systems, creating a tangible sustainability and cost-saving operational model.
This page describes a custom workflow that continuously simulates future merchant revenue using probabilistic forecasts of both generation and market prices, outputting risk metrics like cashflow at risk. It details the Monte Carlo simulation architecture and its value for hedge planning and investor reporting in merchant renewable projects.
This page outlines a custom workflow that ingests preliminary interconnection applications and utility study reports, using NLP to extract upgrade requirements and automatically generate cost estimates. It details the document understanding agents and their role in accelerating the early-stage development process and improving cost forecasting accuracy.
This page details a custom workflow for retail energy providers, where agents analyze a household's smart meter data to model their load profile and automatically recommend a tailored solar or green energy subscription plan. It covers the data analytics, offer generation, and CRM integration needed to scale personalized customer acquisition.
This page covers a critical integration workflow where agents manage authentication, data polling, error handling, and command submission across the complex APIs of different grid operators. It details the robust, fault-tolerant architecture required for production-grade automation, a foundational build for any company automating trading or plant operations.
This page explains a custom workflow for pumped hydro facilities, where agents optimize the timing of pump and generate cycles based on multi-day price forecasts, water levels, and hydrological constraints. It details the long-horizon optimization logic and integration with dam control systems, maximizing the value of this large-scale, long-duration storage asset.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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