Description:FIELD OF INVENTION
The present invention relates to the field of intelligent energy management systems, particularly focusing on the harvesting, storage, and optimization of energy in smart industrial environments. More specifically, the invention leverages artificial intelligence (AI) and machine learning (ML) techniques to enhance the efficiency and reliability of energy harvesting from renewable and ambient sources, optimize energy storage operations, and intelligently distribute energy to industrial loads.
BACKGROUND OF THE INVENTION
In recent years, the advancement of smart industrial environments, often referred to as Industry 4.0, has brought about an exponential increase in energy consumption due to the widespread deployment of automated machinery, industrial IoT (IIoT) devices, and high-performance computing infrastructure. As industries become more digitized and interconnected, the need for efficient and sustainable energy management systems has become paramount.
Traditional energy systems in industrial settings rely heavily on centralized power grids and manually controlled energy storage systems. These setups often lead to suboptimal energy utilization, increased operational costs, and an overdependence on non-renewable energy sources. Moreover, fluctuations in energy demand and intermittent nature of renewable energy sources pose significant challenges to consistent power delivery and storage management.
While various energy harvesting technologies—such as solar photovoltaic, thermoelectric generators, and piezoelectric systems—have been introduced to capture ambient energy, the lack of an intelligent, real-time control mechanism limits their practical effectiveness in dynamic industrial environments. Additionally, conventional energy storage systems often suffer from inefficient charge/discharge cycles and rapid degradation due to improper load balancing and lack of predictive maintenance.
Artificial Intelligence (AI) offers a transformative approach by enabling predictive analytics, real-time optimization, and adaptive control strategies for both energy harvesting and storage systems. However, current implementations of AI in industrial energy management are either fragmented or limited in scope, failing to fully integrate energy harvesting, storage, and load optimization into a cohesive, autonomous framework.
Accordingly, there exists a need for a comprehensive method and system that can intelligently harvest, store, and distribute energy in smart industrial environments using AI-driven models. Such a system would reduce energy waste, extend the lifespan of storage components, minimize operational costs, and enhance overall energy resilience and sustainability.
OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative
An object of the present invention is to provide an intelligent and adaptive method for optimizing energy harvesting and storage in smart industrial environments using artificial intelligence (AI).
Another object of the invention is to maximize the utilization of renewable and ambient energy sources by predicting energy availability and dynamically adjusting harvesting operations in real-time.
A further object of the invention is to enhance the efficiency and lifespan of energy storage systems through AI-driven predictive analytics, load balancing, and intelligent charge/discharge cycle management.
Yet another object of the invention is to reduce dependency on conventional power grids and lower overall operational costs by enabling autonomous energy decision-making and real-time energy flow control.
Another object is to integrate machine learning (ML) and reinforcement learning (RL) techniques to continuously improve energy prediction, load forecasting, and optimal energy distribution strategies within industrial setups.
It is also an object of the invention to provide seamless communication between energy harvesting units, storage devices, and industrial loads using IoT and edge computing technologies, thereby enabling responsive and scalable energy management.
Still another object of the invention is to support smart grid interaction and energy trading, allowing the system to participate in demand response programs and supply excess energy back to the grid or nearby industrial units.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
The present invention provides a novel method and system for AI-optimized energy harvesting and storage tailored to the needs of smart industrial environments. The invention integrates artificial intelligence (AI), including machine learning (ML) and reinforcement learning (RL), with a multi-source energy harvesting architecture and an intelligent energy storage management system to achieve optimal energy efficiency, sustainability, and operational reliability.

The system comprises energy harvesting units capable of collecting power from multiple renewable and ambient sources such as solar, thermal, piezoelectric, and vibration-based systems. Real-time environmental and operational data is collected via a network of IoT-enabled sensors and edge devices. This data is processed by an AI controller that predicts energy generation, analyzes energy demand, and determines the optimal harvesting and storage strategy.
The energy storage system includes hybrid configurations (e.g., lithium-ion batteries and supercapacitors), and the AI controller intelligently manages charging and discharging cycles based on predictive algorithms, system health diagnostics, and industrial load requirements. By employing deep learning models (e.g., LSTM) and reinforcement learning agents, the system continuously learns and adapts to changing environmental conditions and load behaviors, ensuring real-time responsiveness and long-term performance optimization.
Additionally, the invention enables smart load management by forecasting short-term and long-term energy demands and dynamically distributing energy based on load priority, criticality, and resource availability. It also supports grid interaction, allowing excess energy to be stored, utilized, or exported, and enables participation in energy trading and demand response programs.
Through intelligent orchestration of harvesting, storage, and consumption, the system significantly reduces energy wastage, improves storage lifespan, minimizes grid dependency, and supports scalable, autonomous energy operations within Industry 4.0 frameworks.
BRIEF DESCRIPTION OF THE DRAWINGS
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred to by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference.
DETAILED DESCRIPTION OF THE INVENTION
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.
The present invention discloses a method and system for intelligent, AI-based energy harvesting and storage management designed for application in smart industrial environments. The system comprises multiple layers and modules that collectively enable autonomous, efficient, and sustainable energy operations.
1. System Architecture
The system includes the following core components:
• Energy Harvesting Units (EHUs):

These include solar photovoltaic panels, thermoelectric generators, piezoelectric transducers, and vibration-based harvesters installed at key industrial locations. The EHUs are responsible for converting ambient energy sources into usable electrical energy.
• Energy Storage System (ESS):

A hybrid energy storage configuration using lithium-ion batteries, supercapacitors, and optionally hydrogen fuel cells. The ESS ensures energy buffering, balancing, and emergency backup.
• AI Controller Unit:

A centralized or distributed computational module, integrated with AI algorithms, that processes sensor data, predicts energy generation and consumption, and controls energy routing and storage.
• Industrial Load Network:
Comprising machines, robotics, HVAC systems, lighting, and other power-consuming devices, the load network is prioritized based on criticality, operational schedule, and real-time demand.
Smart Grid Interface: Enables bidirectional energy flow to and from the grid, facilitating grid support, demand response participation, and energy trading.
2. AI-Driven Energy Harvesting Optimization
Real-time environmental data such as solar irradiance, ambient temperature, and vibration intensity are collected via sensors. The AI controller uses this data along with historical trends to:
• Predict the short-term and long-term energy generation potential from each EHU.
• Dynamically allocate harvesting tasks to the most optimal sources.
• Adjust energy harvesting parameters to maximize conversion efficiency under variable conditions.
Machine learning models such as Long Short-Term Memory (LSTM) networks and XGBoost regressors are employed for predictive modeling.

3. Smart Energy Storage Management

The hybrid ESS is managed by the AI controller to:
• Predict the state-of-charge (SoC), state-of-health (SoH), and charge/discharge efficiency.
• Optimize energy routing between multiple storage devices.
• Apply Reinforcement Learning (RL) algorithms (e.g., Q-learning, Deep Q-Networks) to minimize energy losses and battery degradation.
Charge-discharge cycles are adjusted based on load demand forecasting and energy price fluctuations if grid-connected.
4. Load Forecasting and Energy Distribution
The system forecasts industrial load requirements using a combination of:
• Historical operational data.
• Real-time process metrics.
• Shift schedules and machine utilization logs.
Based on this, the system performs:
• Load prioritization (critical, semi-critical, non-critical).
• Energy allocation using AI decision models.
• Demand-side management, especially during peak loads or energy shortages.
5. Intelligent Energy Flow Control
A control algorithm continuously optimizes the energy flow across the system:
• If harvested energy exceeds demand, surplus is stored or exported to the grid.
• If demand exceeds harvesting, stored energy is used based on priority rules.
• If neither is sufficient, grid energy is drawn based on real-time pricing and demand-response signals.
This feedback-based control loop continuously learns and adapts, ensuring self-healing and autonomous operation.
6. Edge Computing and Communication
• Edge computing devices are deployed near EHUs and ESS for latency-sensitive tasks.
• Communication protocols such as LoRa, Zigbee, MQTT, and OPC UA ensure secure, low-power data exchange.
• The AI controller can reside in the cloud or locally on high-performance edge nodes, depending on latency and connectivity constraints.
7. Grid and Microgrid Integration
• The system can operate independently (islanded) or in a grid-tied configuration.
• Real-time synchronization with smart grid controllers ensures:
o Export/import of energy.
o Participation in real-time energy markets.
o Compliance with grid stability requirements.
8. Monitoring and User Interface
A dashboard provides real-time monitoring of:
• Energy harvested, stored, and consumed.
• AI predictions and decisions.
• Alerts on system performance, faults, and maintenance needs.
The user can override or adjust parameters manually or set custom operating rules.
Advantages of the Invention
• Efficient utilization of renewable and ambient energy sources.
• Reduced dependency on grid energy and lower energy costs.
• Increased lifespan of energy storage systems via AI-based cycle optimization.
• Enhanced reliability and sustainability in industrial energy operations.
• Seamless integration with existing industrial automation and IIoT infrastructure.

While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

, Claims:We Claim,
1. Method for AI-optimized energy harvesting and storage in smart industrial environment, comprising:
harvesting energy from one or more renewable or ambient sources selected from solar, thermal, piezoelectric, or vibration-based sources using a plurality of energy harvesting units (EHUs);
monitoring environmental and operational parameters using a network of sensors to collect data relating to energy generation, energy storage status, and industrial load demands;
processing the collected data through an artificial intelligence (AI) controller configured with machine learning models to:
a. forecast energy generation potential; and
b. predict load requirements based on historical and real-time industrial activity;
dynamically optimizing the operation of the energy harvesting units in accordance with the forecasted environmental conditions to maximize energy collection;
managing a hybrid energy storage system comprising at least one of lithium-ion batteries and supercapacitors, including:
c. Regulating charge and discharge cycles;
d. Predicting state-of-charge (SoC) and state-of-health (SoH); and
e. Preserving storage efficiency and lifetime using AI-based decision strategies;
distributing energy to connected industrial loads based on prioritization determined by the AI controller, wherein the loads are classified and supplied according to their operational criticality and energy availability;
interfacing with a smart grid or microgrid to enable energy import or export and participating in energy trading or demand response programs;
adaptively refining the AI models over time through reinforcement learning or continuous training based on system feedback.
2. The method of claim 1, wherein the AI controller employs Long Short-Term Memory (LSTM) networks or Gradient Boosting models to forecast energy generation and load demand with high accuracy.

3. The method of claim 1, wherein the energy harvesting units are equipped with adaptive control algorithms to autonomously reconfigure their operating parameters based on real-time environmental feedback.

4. The method of claim 1, wherein the hybrid energy storage system includes supercapacitors configured for short-term high-power output and batteries configured for long-term energy retention.

5. The method of claim 1, wherein the AI controller utilizes reinforcement learning algorithms to optimize energy allocation and storage management strategies based on reward feedback mechanisms.

6. The method of claim 1, wherein the distribution of energy to industrial loads is prioritized based on a criticality matrix defined by real-time process requirements, safety protocols, and energy costs.

7. The method of claim 1, wherein the method further comprises detecting system anomalies, failures, or deviations using AI-based predictive maintenance models, and initiating corrective actions.

Dated this: 11-04-2025

Dr. Amrish Chandra
IN/PA 2959