Problem statement
Encouraging the adoption of sustainable technologies is critical for environmental conservation. This project aims to develop an AI-based recommendation system that suggests sustainable technologies based on user preferences, business requirements, and environmental impact.
Abstract
The project addresses the need for promoting sustainable technology adoption by leveraging AI algorithms. The recommendation system analyzes user preferences, business goals, and environmental considerations to suggest technologies that align with sustainability objectives. The goal is to facilitate informed decision-making in adopting eco-friendly technologies.
Outcome
- AI-based recommendation system for sustainable technology adoption.
- Informed decision-making for businesses and individuals regarding eco-friendly technologies.
- Promotion of environmental sustainability through technology choices.
References
The electric grid has already been transitioned towards a more flexible, intelligent, and interactive grid system, i.e., Smart Grid (SG) for load management, energy prediction, higher penetration of renewable energy generation, future planning, and operations. However, there is a huge gap between energy demand and supply due to the rise of different electric products and electric vehicles. Renewable Energy Harvesting (REH) plays a critical role in managing this demand response gap, where energy is generated from various renewable energy resources such as Solar PhotoVoltaic (SPV) and wind energy. Several research works exist in this regard. However, they have not yet been exploited fully. So, this paper proposed AI-RSREH approach, i.e., the AI-empowered Recommender System for REH in residential houses. The main goal of the proposed AI-RSREH approach is to predict energy generation based on SPV accurately, and this study aims to minimize the gap between the actual generation of energy and the predicted energy generation along with a recommender system for SPV installation. An exploratory residential house-wise data analytics is conducted for the demand response gap. AI-RSREH uses a stacked Long-Short Term Memory (LSTM) model to predict energy generation with a recommender system based on the energy generation prediction result. The obtained results show the efficacy of the proposed approach compared to the existing methods with respect to parameters such as SPV installation in residential houses and prediction accuracy.
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