SPOT-ENERGY: A User-Centric Platform for Energy Simulation, Prediction and Optimization

Abstract

SPOT-Energy is a scalable, modular platform for appliance-level energy simulation, forecasting, and cost optimization. It leverages a Conditional Variational Autoencoder (CVAE) to model behavioral demand fluctuations, and combines Prophet time-series analysis with Long Short-Term Memory (LSTM) networks and CatBoost regressors for accurate, appliance-specific forecasts. Its intuitive web interface provides interactive dashboards, detailed consumption insights, cost projections, and AI-driven recommendations. Custom alerts and budget-tracking tools enable timely user interventions and promote energy-conscious habits. Validation on synthetic user profiles confirms SPOT-Energy’s ability to uncover inefficiencies and guide proactive, data-driven energy decisions.

Introduction

The growing global demand for energy, driven by infrastructure expansion, technological advances, and stricter sustainability goals, requires more advanced energy management strategies. Modern pricing models, such as dynamic tariffs, add complexity to consumption planning. Despite abundant energy usage data, varied consumption patterns across households, businesses, and regions make it difficult for end users to extract actionable insights without specialized tools.

To address this, the paper introduces SPOT-Energy, a user-centric software platform that integrates personalized consumption simulation, forecasting, optimization, and visualization. SPOT-Energy combines deterministic calculations and machine learning (notably Conditional Variational Autoencoders and models like LSTM, Prophet, and CatBoost) to generate realistic appliance-level energy usage scenarios, forecast future consumption, and recommend cost- and carbon-saving operational schedules. Its interactive dashboard helps users identify high-impact devices and evaluate alternative scenarios.

The platform’s modular design supports diverse users, from residential consumers to large planners, adapting to individual preferences, regional tariffs, and environmental conditions. Evaluations demonstrated SPOT-Energy’s accuracy in simulation, robustness in forecasting (achieving errors below 10%), and effectiveness in optimization—delivering average energy cost reductions of 18–25% while preserving user comfort. It also promotes sustainability by providing CO? emission impact insights and personalized action plans, all without requiring additional hardware.

Conclusion

SPOT-Energy represents a significant advancement in intelligent, user-centric energy management by offering a fully software-based solution that combines behavioral simulation, predictive modeling, and optimization. Its modular architecture, dynamic adaptability, and real-time insights empower users to make informed decisions without reliance on physical sensors or IoT infrastructure. By translating complex energy data into actionable recommendations, SPOT-Energy bridges the gap between analysis and awareness—ultimately encouraging cost-conscious and sustainable consumption. This positions the framework as a practical, scalable tool for energy-conscious households, with strong potential for broader adoption and future integration into smart city initiatives.

References

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Copyright

Copyright © 2025 Prof. Sharvani V, Dr. Girish Kumar D, Ms. Tannu Chaudhary, Prof. Subhashree D C, Prof. M M Harshitha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.