A professional, full-stack web application for forecasting power consumption using advanced ARIMA and SARIMAX time series forecasting models. This system provides accurate predictions based on weather-related features and offers a modern, responsive web interface for real-time forecasting.

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An AI/ML-based energy consumption prediction system that analyzes past data to forecast future power usage and improve energy efficiency.

The Smart Home Energy Optimizer is an AI-powered application designed to predict and optimize household energy consumption. This system leverages time-series forecasting using the Prophet model to analyze historical energy usage data and generate accurate future consumption predictions.

A web-based form for predicting active power consumption based on inputs like voltage, current, and frequency. It uses a machine learning model to provide real-time predictions. The form features a clear button for resetting inputs and displaying predicted results for power system analysis.

Built a residential energy consumption forecasting system using LSTM and Random Forest on the UCI Household Power dataset. Implemented preprocessing, feature engineering (lag & time features), and model evaluation (MAE, RMSE, MAPE). Random Forest outperformed LSTM for short-term hourly load prediction.

SmartGridGuard is an AI-powered system prototype for short-term energy load forecasting and anomaly detection. It uses LSTM deep learning for accurate demand prediction and Isolation Forest for detecting irregular consumption patterns, enabling smarter, more efficient, and reliable energy management through real-time insights.

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Enterprise Energy Manager is an AI-powered system that forecasts commercial energy usage to reduce costs. Built with XGBoost, FastAPI, and Streamlit, it predicts 24-hour load profiles based on weather and occupancy. Features include real-time cost estimates, interactive planning graphs, and AI insights for smarter facility management.

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Energy Consumption Prediction system project that predicts daily power consumption

AI-powered electricity consumption prediction and anomaly detection system.

machine learning-powered energy consumption prediction system that analyzes historical data to forecast future energy usage trends, optimizing efficiency and sustainability.

ML-powered energy consumption forecasting system for Granada Smart City. FastAPI backend with PostgreSQL integration, real-time predictions, and interactive dashboard for urban energy analytics.

AI-powered energy management system with Spring Boot, React, and PostgreSQL. Provides smart dashboards, electricity usage tracking, bill estimation, consumption predictions, and AI-based energy-saving suggestions.

This project is a smart energy prediction system that estimates household electricity usage and monthly bill based on real historical power consumption data. The system uses a trained Machine Learning model (Random Forest, XG Boost, LSTM) to predict instantaneous power usage and an ARIMA time-series model to forecast future monthly consumption.

A real-time IoT-based Smart Energy Monitoring System that measures voltage, current, power, and energy consumption using ESP32 and cloud integration. The system uses machine learning models for cost prediction and consumption classification, and provides instant alerts to users through a web dashboard and mobile notifications.

AI-powered Food Waste Prediction System that analyzes historical consumption data to forecast demand and help restaurants/hostels reduce overproduction. Optimizes inventory planning, minimizes waste, and promotes sustainable food management.

AI-Powered OS Monitor is a web-based system monitoring tool that provides real-time insights into system performance. It tracks CPU usage, memory, disk usage, network activity, and power consumption while also utilizing AI-based predictions to detect anomalies, predict system overloads, and suggest optimizations.

An Logic-driven adaptive power management system that dynamically optimizes processor performance using workload prediction, DVFS, and clock gating. The project integrates Verilog-based hardware simulation with a Python backend and a real-time web dashboard to visualize power consumption and system behavior

An AI-powered campus energy analytics and prediction system that leverages XGBoost to forecast energy consumption based on temporal features. It includes an interactive Streamlit dashboard with visual insights, building-wise analysis, and future predictions to enable smarter energy optimization decisions.

A Delhi Electricity Load Prediction System is designed to forecast future power consumption trends using historical load data, weather patterns, and seasonal variations. By employing machine learning models like XGBoost, LSTM, and time-series analysis, this system provides accurate predictions to optimize energy distribution and planning.

The "Solar Power Prediction using Linear Regression" project uses machine learning to predict solar power generation by modeling factors like weather, time of day, and location. The goal is to create a system that optimizes solar energy generation and consumption.

AI-powered Energy Consumption Optimization system that predicts building energy usage based on environmental and operational inputs. Built using Python, Flask, and Scikit-learn, it provides real-time predictions, optimization recommendations, and deployment-ready web dashboard.

Hostel Electricity Usage Analysis & Prediction System analyzes historical power data to identify usage patterns and predict future consumption using regression models. It includes preprocessing, feature engineering, model evaluation, and organized report generation for efficient energy management.

Smart Society Alert & Resource System (SSARS) is an intelligent Django-based platform for energy monitoring, ML-powered consumption forecasting, real-time weather tracking, news aggregation, and smart optimization. Offers dashboards, alerts, predictions, and efficient resource management.

An end-to-end machine learning system for household energy consumption monitoring, analysis, and forecasting using LSTM neural networks. This project provides device-level insights, AI-powered predictions, and personalized energy-saving recommendations through an interactive web dashboard.

AI-powered Smart Energy Management System that predicts electricity consumption using Machine Learning models like Random Forest and XGBoost. Includes a Flask API and Streamlit dashboard for real-time predictions, visualization, and energy optimization to support sustainable smart cities.