EcoGrid AI

\documentclass[12pt]{article} \usepackage[margin=1in]{geometry} \usepackage{hyperref} \usepackage{enumitem}

\title{\textbf{EcoGrid AI – AI-Driven Short-Term Energy Load Forecasting & Anomaly Detection}} \author{} \date{}

\begin{document}

\maketitle

\section*{1. Problem Statement} Buildings in India consume nearly one-third of total energy and contribute significantly to carbon emissions. Inefficient energy usage leads to high electricity bills, stress on the power grid, and reliance on fossil fuels. Existing energy management systems are often building-specific, non-adaptive, and cannot scale easily, making it difficult to optimize energy use across diverse Indian buildings.

\section*{2. Solution} \textbf{EcoGrid AI} is an AI-powered system designed to accurately forecast short-term energy consumption, detect anomalies, and provide actionable insights for smarter energy management.

\begin{itemize}[leftmargin=*] \item \textbf{Adaptive Short-Term Forecasting:} Uses pre-trained Time Series Foundation Models (TSFMs) to predict hourly or daily energy consumption for residential and commercial buildings. Forecasts adapt based on occupancy, weather, and events. \item \textbf{Intelligent Anomaly Detection:} Compares predicted vs actual energy usage to identify unusual spikes or drops. Provides probable causes such as faulty equipment, unexpected occupancy, or extreme weather. \item \textbf{Actionable Energy Recommendations:} Suggests energy-saving measures and optimal usage of renewable energy sources like solar panels or battery storage. \item \textbf{Scalable and Generalizable:} Applicable across multiple building types in India without retraining, with optional fine-tuning for building-specific optimization. \item \textbf{Interactive Dashboard:} Displays forecasts, anomalies, and recommended actions in a user-friendly interface. \end{itemize}

\section*{3. Methodology} \begin{itemize}[leftmargin=*] \item \textbf{Data Preprocessing:} Cleaning, normalization, and generation of temporal and weather-related features. \item \textbf{Modeling:} TSFMs for forecasting; baseline models (ARIMA, LSTM, Prophet) for performance comparison. \item \textbf{Evaluation:} Forecast accuracy measured with MAE, RMSE, MAPE; anomaly detection evaluated with precision, recall, and F1-score. \item \textbf{Visualization:} Streamlit or Dash dashboard for interactive display of insights. \end{itemize}

\section*{4. Dataset} \begin{itemize}[leftmargin=*] \item Smart meter readings, appliance-level energy data, HVAC usage. \item Weather features: temperature, humidity, solar radiation. \item Optional metadata: building type, occupancy, floor area. \end{itemize}

\section*{5. Innovation & Impact} \begin{itemize}[leftmargin=*] \item \textbf{Adaptive Forecasting:} Adjusts predictions dynamically based on real-world factors. \item \textbf{Cause Attribution:} Explains likely reasons behind anomalies. \item \textbf{Renewable Energy Optimization:} Integrates solar and battery storage to reduce fossil fuel dependence. \item \textbf{Scalable Solution:} Generalizes across multiple buildings in India without retraining. \item \textbf{Socio-Economic Impact:} Reduces energy bills, eases grid stress, and contributes to decarbonization. \end{itemize}

\section*{6. India-Focused Benefits} \begin{itemize}[leftmargin=*] \item Optimizes energy usage for residential and commercial buildings. \item Supports solar and battery integration for greener energy use. \item Helps align with government initiatives like Smart Cities Mission and energy efficiency programs. \item Promotes sustainable and cost-effective energy management nationwide. \end{itemize}

\section*{7. Expected Outcomes} \begin{itemize}[leftmargin=*] \item Functional AI prototype capable of forecasting energy usage and detecting anomalies. \item Dashboard providing real-time insights and actionable recommendations. \item Scalable deployment for multiple building types in India, enabling energy efficiency, cost savings, and reduced carbon emissions. \end{itemize}

\end{document}