Sung Hyup Hong, Byeongmo Seo, Ho Sung Jeon, Jong Min Choi, Kwang Ho Lee, Donghyun Rim
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引用次数: 0
摘要
通过利用社区单位的光伏发电和 ESS,对电能节约情况进行了评估。采用人工神经网络(ANN)和长短期记忆(LSTM)创建了光伏发电预测模型。住宅建筑的年需求数据是通过 EnergyPlus 估算的,而其他建筑的数据则是通过在大韩民国 J Energy Town 的测量收集的。皮尔逊相关系数为模型确定了六个关键变量。对 310 个案例的比较分析表明,表现最好的模型是一个有三个隐藏层、节点数分别为 14、13 和 11 的 ANN。该模型符合 ASHRAE 准则,CV(RMSE)为 29.1%,NMBE 为 -7.14%。在对社区用电量进行评估时,案例 B(光伏发电)比案例 A 显著减少了 46.3%,而案例 D 在一年中比案例 E 节省了 5%的能源。
Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions
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.
期刊介绍:
The aim of the Journal of Mechanical Science and Technology is to provide an international forum for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of mechanical engineering, either empirical or theoretical. The Journal covers the whole spectrum of mechanical engineering, which includes, but is not limited to, Materials and Design Engineering, Production Engineering and Fusion Technology, Dynamics, Vibration and Control, Thermal Engineering and Fluids Engineering.
Manuscripts may fall into several categories including full articles, solicited reviews or commentary, and unsolicited reviews or commentary related to the core of mechanical engineering.