It is vital for Utilities & Grid Operators, as well as the facility functions of large industrial energy users, to understand the health of Transformers in their networks and receive early indicators if a particular Transformer is overloaded. Our newest insight is now available to identify Transformers that are already overloaded or are at risk of being overloaded. Using this insight will assist in preventing damage as well as degradation of transformer lifespan, thereby minimizing safety risks, as well as driving optimal resource utilization, thus ultimately improving the reliability of the grid.

The grid transition is changing how and when electricity is generated and consumed. This makes planning and operating the grid, managing trading risk and reducing scope 2 emissions increasingly challenging. Innowatts BTM Identification provides meter level visibility to which meters have Electric Vehicles (EVs), PhotoVoltaics (PVs) and Heat Pumps. We use these Insights to inform our forecasts, identify flexibility, and provide our customers the data they need to understand and target the end users with appropriate products and services.

Understanding how each meter’s load changes in relation to each degree increase or decrease in temperature is a key input into our award winning forecasts. This Insight can be used by our customers to identify which meters are best suited for various interventions and products. For example, the deployment of local storage or if they are suited for thermal measures including the provision of upgraded HVAC for efficiency, increased insulation, pre-heating / cooling routines etc.

With the drive to net zero and the increasing reliance on renewable energy, matching supply to demand is becoming increasingly challenging. To counter this, market mechanisms to support load flexibility are on the rise. As the balance shifts from thermal to renewable generation, participants need to access the flexibility of smaller and more distributed devices. Innowatts BTM Insights identify which meters have optionality now and in the future. Alerts from these Insights can be pushed to control systems to drive action.

Connecting to data at a granular level enables Innowatts AI to Extract Insights into the usage and generation of electricity behind every meter. Be that a meter at a home, office, commercial or industrial facility or from within the power network. These Insights, in conjunction with our proprietary weather service and bottoms up approach, enables us to deliver award winning forecast accuracy at any level of aggregation within your portfolio and for any time horizon.

The proliferation of Photovoltaics (PVs), Batteries, and other Behind the Meter (BTM) generation, has reached a point where they are having a meaningful impact on demand forecasts. Innowatts ability to identify BTM devices and generate individual forecasts, helps to ensure that the net demand is accurately comprehended. Additionally the generation forecast acts as a key input in the forecasting of load optionality.

Traders and Risk Managers need to anticipate likely wholesale price moves and system peaks. A key factor in driving these pricing moves is the total system load for the market in which you are operating. Innowatts provides an industry leading 14 day ISO Forecast with proven performance during extreme weather events.

By combining historic usage data, portfolio growth estimates and our BTM Insights, the Scenario Analysis service enables you to model the impact of a variety of weather patterns on your energy portfolio. Scenarios can be applied and analyzed for forecasted load and or generation forecasts from meter level right up through system level views.

In many markets, the cost of Transmission and Distribution (T&D) charges is calculated based on the recorded usage for an individual meter at the times of system peaks within specified months of the year. Innowatts System Forecasts combined with Meter Level Forecasts and Load Optionality provide you with the ability to understand when these peaks will occur, what load you can shift and drive action to reduce your T&D charges.

Drive load changes by understanding which meters have future flexibility, and push notifications to end users and control systems. Our BTM Insights identify and forecast load optionality of BTM devices, and by using our platform’s Alerting and Integration functions, users can drive meaningful change in load shapes and reduce their cost of supply. 

Innowatts AI extracts Insights to identify behind which meters flexible load resides. Our forecasting services then determine the optimum times for these assets to support demand response without causing interruption to the end users service. Availing of our Alerting and Integration services you can push notifications to your demand response provider to inform them which loads, generation or storage to activate and when.

The Innowatts Carbon Insights and granular meter-level approach is an invaluable tool to assist you in achieving your decarbonization goals. Our Insights can enhance your carbon accounting, thereby identifying and eliminating Scope 2 carbon emissions. The Insights also enable a detailed DER deployment strategy to further reduce carbon and promote load optionality. Thanks to the Carbon ID feature, the Innowatts Platform will report outlier usage and carbon production, allowing for better allocation of capital to further combat carbon and promote sustainability.  

For Retailers, our Profitability Insight works at a meter level to enable you to develop marketing strategies to retain and grow your most profitable customers. Trading and Risk Teams can use our forecasts to reduce imbalance charges, inform hedge trades, monitor Net Open Position and Mark-to-Market.

Our platform enables you to distill Insights to identify customers of interest by using InnoPODs – Innowatts Personalized Opinionated Datasets. Information from an InnoPOD can be visualized via a variety of popular business intelligence tools, for example Power BI, or shared via API or pushed to third party systems to trigger downstream workflows.