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A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs
Author(s) -
Duhirwe Patrick Nzivugira,
Hwang Jun Kwon,
Ngarambe Jack,
Kim Suhgoo,
Kim Kyungjae,
Song Kwanwoo,
Yun Geun Young
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6462
Subject(s) - photovoltaic system , automotive engineering , energy storage , fossil fuel , energy (signal processing) , energy accounting , environmental science , energy consumption , efficient energy use , process engineering , computer science , reliability engineering , engineering , electrical engineering , waste management , power (physics) , mathematics , statistics , physics , quantum mechanics
Summary The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV‐generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV‐generated energy. We propose a novel integrated energy‐efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)‐based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV‐generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL‐based forecasting models for PV and EHP are accurate and reliable (ie, R 2 above 0.95). Also, the results show that the proposed integrated energy‐efficient PV‐ESS‐EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV‐generated energy resulting in reduced dependency on fossil fuels for building energy needs.