Description:FIELD OF THE INVENTION
This invention relates to IoT-Enabled Solar PV Forecasting and Load Monitoring for Optimized Energy Management
BACKGROUND OF THE INVENTION
The proposed system is designed for real-time Solar PV system and Load monitoring, Using the cloud for a stand-alone solar PV setup with a boost converter and Maximum Power Point Tracking.
Currently, several commercial products and integrated solutions are available for real-time solar PV system monitoring, energy management, and forecasting. Products such as SolarEdge Monitoring Platform, SMA Sunny Portal, and IoT-based smart energy meters offer real-time monitoring, data analytics, and predictive maintenance for solar PV setups. Additionally, MPPT-based charge controllers like Victron SmartSolar MPPT and Outback FlexMax optimize battery charging efficiency.
The present commercial practice involves the integration of IoT-based smart controllers, cloud-based monitoring systems, and MPPT-enabled boost converters to enhance solar PV system performance. Many of these solutions also incorporate machine learning and AI-based forecasting to predict solar energy generation and consumption patterns, improving overall energy planning. However, existing systems often lack seamless integration of IoT with solar ,MPPT boost converters, voltage/current controllers,Loads and predictive analytics, highlighting the need for an advanced solution like the proposed system, which ensures efficient power management, real-time monitoring, and accurate forecasting for improved energy utilization.
PRIOR ART
US20230120453: A system includes control circuitry configured to manage faults of an electrical system. The system is configured to monitor consumption for a plurality of electrical circuits, such as branch circuits, and generate device information about a device based on an electrical current measurement from at least one electrical circuit of the plurality of electrical circuits to which the device is coupled. The system is also configured to determining that an event has occurred based on the device information and interrupt current of the at least one electrical circuit, generate a notification, communicate a control signal to the device in response to the event occurring to mitigate the event, actuate a second device in response to the event, or a combination thereof.
US20230120740: A system includes control circuitry configured to determine a maximum electrical load for one or more electrical circuits on one or more electrical panels, determine preference information for allocating the maximum electrical load, automatically set a charge rate for charging the electric vehicle using current from at least one of the one or more electrical circuits based on the maximum electrical load and on the preference information, and cause the electric vehicle to be charged at the charge rate.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed system addresses existing limitations by integrating IoT-based Could monitoring, MPPT control, AI-driven forecasting, and adaptive energy management into a unified solution. It seamlessly connects MPPT-enabled boost converters with smart IoT controllers, ensuring real-time voltage and current optimization. An AI-powered forecasting model predicts solar generation and load demand using historical and weather data, enhancing energy planning. A central energy management unit efficiently distributes power between the grid, battery storage, and loads. The system is cost-effective and scalable, using low-cost microcontrollers and cloud-based analytics. Unlike traditional solutions, it features real-time adaptive control, dynamically adjusting power flow for maximum efficiency and stability, ensuring optimized energy utilization, reduced costs, and improved reliability in solar PV systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed system addresses existing limitations by integrating IoT-based Could monitoring, MPPT control, AI-driven forecasting, and adaptive energy management into a unified solution. It seamlessly connects MPPT-enabled boost converters with smart IoT controllers, ensuring real-time voltage and current optimization. An AI-powered forecasting model predicts solar generation and load demand using historical and weather data, enhancing energy planning. A central energy management unit efficiently distributes power between the grid, battery storage, and loads. The system is cost-effective and scalable, using low-cost microcontrollers and cloud-based analytics. Unlike traditional solutions, it features real-time adaptive control, dynamically adjusting power flow for maximum efficiency and stability, ensuring optimized energy utilization, reduced costs, and improved reliability in solar PV systems.
The proposed system uniquely integrates real-time IoT-enabled MPPT control, AI-based solar forecasting, and adaptive energy management
The present invention relates to an intelligent solar photovoltaic (PV) energy management system that integrates Internet of Things (IoT), maximum power point tracking (MPPT), and artificial intelligence (AI) forecasting to achieve optimized solar energy utilization.
Conventional solar PV monitoring systems provide partial solutions by enabling either MPPT-based control or IoT-based data monitoring. However, such systems lack unified control, adaptive forecasting, and predictive analytics, leading to inefficiencies and reduced reliability in power management.
The invention addresses these shortcomings by providing an integrated system that includes an MPPT-enabled boost converter, IoT controllers, AI-based forecasting models, and a centralized energy management unit. The MPPT converter ensures that the PV array operates at maximum efficiency under varying environmental conditions. The IoT module collects real-time voltage, current, and load data and transmits it to the cloud for monitoring and advanced analytics.
The forecasting unit employs machine learning algorithms to predict both solar generation and load demand using historical data, weather conditions, and consumption trends. Based on this predictive analysis, the energy management unit intelligently controls the distribution of power among the grid, storage devices, and loads. This ensures stable operation, cost savings, and enhanced reliability in residential, commercial, and grid-integrated applications.
The best method of implementing the invention involves connecting the solar PV array to an MPPT-based boost converter that regulates voltage and current for maximum power output. The converter is interfaced with a microcontroller (ESP32 or Raspberry Pi), which collects real-time operational data.
This data is transmitted to a cloud platform via IoT protocols (such as MQTT), enabling visualization and analytics. A machine learning model trained with historical energy and weather datasets forecasts energy generation and consumption trends. Based on these predictions, the centralized energy management unit dynamically controls energy allocation between grid supply, battery storage, and load circuits.
The modular design ensures easy scalability, while the cloud-based interface allows users to remotely monitor system health, performance, and energy optimization parameters.
ADVANTAGES OF THE INVENTION
 Seamless IoT-MPPT Integration – Unlike existing solutions that operate IoT-based monitoring and MPPT control separately, the proposed system combines them for real-time optimization, enhancing solar PV efficiency.
 AI-Driven Forecasting – Incorporates machine learning-based solar generation and load demand prediction, improving energy planning and utilization compared to traditional static forecasting methods.
 Adaptive Energy Management – Features a centralized energy management unit (EMU) that dynamically controls power flow between PV, battery, and grid, ensuring optimal performance under varying conditions.
 Cost-Effective and Scalable Design – Uses low-cost microcontrollers (ESP32, Raspberry Pi) and modular architecture, making it more affordable and scalable than high-cost commercial systems.
, C , Claims:1. An IoT-enabled solar photovoltaic energy management system comprising a maximum power point tracking (MPPT) based boost converter, an IoT controller for real-time monitoring of voltage, current, and load parameters, an artificial intelligence forecasting module configured to predict solar generation and load demand using historical and weather data, and a centralized energy management unit adapted to dynamically control and distribute energy between the solar PV array, battery storage, grid, and connected loads to optimize efficiency and reliability.
2. The system as claimed in claim 1, wherein the IoT controller is configured to transmit real-time operational data to a cloud-based platform for monitoring and analytics.
3. The system as claimed in claim 1, wherein the MPPT-based boost converter dynamically adjusts solar panel impedance to maximize power extraction under variable irradiance conditions.
4. The system as claimed in claim 1, wherein the AI forecasting module utilizes machine learning algorithms to generate predictions of power generation and consumption.
5. The system as claimed in claim 1, wherein the centralized energy management unit allocates power between grid, storage, and loads based on predictive analytics.
6. The system as claimed in claim 1, wherein low-cost microcontrollers including ESP32 or Raspberry Pi are employed for data acquisition and processing.
7. The system as claimed in claim 1, wherein the IoT controller provides adaptive load control to ensure stable energy utilization under varying solar conditions.
8. The system as claimed in claim 1, wherein the cloud-based monitoring platform supports visualization dashboards for system health and performance indicators.
9. The system as claimed in claim 1, wherein the energy management unit dynamically responds to fluctuations in solar input and load demand to minimize cost and maximize reliability.
10. The system as claimed in claim 1, wherein the system is modular and scalable for integration with residential, commercial, or grid-connected solar PV installations.