Abstract: The embodiments disclosed in this document are directed to an AI-enabled microgrid and DER planning platform that uses AI methods and takes into account cost calculations, emission calculations, technology investments and operation. In an embodiment, the computing platform is deployed on a network (cloud computing platform) that can be accessed by a variety of stakeholders (e.g., investors, technology vendors, energy providers, regulatory authorities). In an embodiment, the planning platform implements machine learning (e.g., neural networks) to estimate various planning parameters, where the neural networks are trained on observed data from real-world microgrid/minigrid and DER projects.

Abstract: Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Abstract: Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

Abstract: Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: Systems and methods for performing power analytics on a microgrid. In an embodiment, predicted data is generated for the microgrid utilizing a virtual system model of the microgrid, which comprises a virtual representation of a topology of the microgrid. Real-time data is received via a portal from at least one external data source. If the difference between the real-time data and the predicted data exceeds a threshold, a calibration and synchronization operation is initiated to update the virtual system model in real-time. Power analytics may be performed on the virtual system model to generate analytical data, which can be returned via the portal.

Abstract: Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: The embodiments disclosed in this document are directed to an AI-enabled microgrid and DER planning platform that uses AI methods and takes into account cost calculations, emission calculations, technology investments and operation. In an embodiment, the computing platform is deployed on a network (cloud computing platform) that can be accessed by a variety of stakeholders (e.g., investors, technology vendors, energy providers, regulatory authorities). In an embodiment, the planning platform implements machine learning (e.g., neural networks) to estimate various planning parameters, where the neural networks are trained on observed data from real-world microgrid/minigrid and DER projects.

Abstract: Systems and methods for a cloud computing engineering application that can provide new interaction, visualization and interaction techniques along with advertising messaging and business social networking are disclosed.

Abstract: Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

Abstract: Systems and methods for performing power analytics on a microgrid. In an embodiment, predicted data is generated for the microgrid utilizing a virtual system model of the microgrid, which comprises a virtual representation of a topology of the microgrid. Real-time data is received via a portal from at least one external data source. If the difference between the real-time data and the predicted data exceeds a threshold, a calibration and synchronization operation is initiated to update the virtual system model in real-time. Power analytics may be performed on the virtual system model to generate analytical data, which can be returned via the portal.

Abstract: Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.

Abstract: A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm.

Abstract: Systems and methods for modeling an electrical power system are disclosed. A computer and an analytics server are in network connection. The computer comprises a processor coupled with a memory. The memory is configured to maintain at least one engine element and a component database. The analytics server comprises a virtual system modeling engine and an analytics engine. The component database is operable to store power system components. The at least one engine element is operable to generate a virtual system model of the electrical power system and generate predicted output based on the virtual system model. The analytics engine is operable to monitor the predicted output and real-time output from at least one sensor of the electrical power system, and calibrate the virtual system model based on a difference between the predicted output and the real-time output.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.