Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions

• Published in: Journal of mechanical science and technology Vol. 38; no. 8; pp. 4431 - 4446
• Main Authors: Hong, Sung Hyup, Seo, Byeongmo, Jeon, Ho Sung, Choi, Jong Min, Lee, Kwang Ho, Rim, Donghyun
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.08.2024; Springer Nature B.V; 대한기계학회
• Subjects: Artificial neural networks; Control; Correlation coefficients; Dynamical Systems; Electricity consumption; Engineering; Industrial and Production Engineering; Mechanical Engineering; Original Article; Prediction models; Predictions; Residential buildings; Residential energy; Vibration; 기계공학; ANN; PCC; LSTM; ESS; Smart city
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-024-0739-z

Electrical energy saving was evaluated by taking advantage of PV and ESS in a community unit. An artificial neural network (ANN) and long short-term memory (LSTM) were employed to create a predictive model for PV generation. Annual demand data for residential buildings were estimated using EnergyPlus, while data for other buildings were collected from measurements in J Energy Town, Republic of Korea. Pearson correlation coefficients identified six crucial variables for the model. Comparative analysis of 310 cases revealed that the best-performing model was an ANN with three hidden layers and nodes of 14, 13 and 11. The model satisfied ASHRAE guidelines with a CV(RMSE) of 29.1 % and NMBE of −7.14 %. Evaluating electricity consumption in the community, case B (PV generation) showed a significant 46.3 % reduction compared to case A, while case D achieved a 5 % energy savings relative to case E over the year.