Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AI-RSREH</i> approach, i.e., the AI-empowered Recommender System for REH in residential houses. The main goal of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AI-RSREH</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AI-RSREH</i> 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.
Highlights
With the increasing electricity demand, Smart Grid (SG) has become an essential technology that allows easier integration and higher penetration of renewable energy to reduce the demand response gap
Once prediction results are obtained from the first stage, a recommender system for the Solar PhotoVoltaic (SPV) installation is proposed in the second stage
Renewable Energy Harvesting (REH) has become a critical component of the SG system for Demand Response Management (DRM)
Summary
With the increasing electricity demand, Smart Grid (SG) has become an essential technology that allows easier integration and higher penetration of renewable energy to reduce the demand response gap. Much research work exists, very few approaches confronted the SPV energy generation for individual customers. It has been considered trivial because of the volatile nature of energy consumption at the customer end. Motivated from the aforementioned discussion, this paper proposes an AI-empowered Recommender System for REH (AI-RSREH) to accurately predict the SPV energy generation in residential houses. AI-RSREH uses the LSTM model for SPV energy generation prediction in the DRM system. Based on the prediction result, it proposes a recommender system to install SPVs in the various building of a particular locality to close the demand response gap
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
