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 EnergyPlu...

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Published inJournal 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
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
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 bestperforming 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. KCI Citation Count: 0
Author Choi, Jong Min
Lee, Kwang Ho
Seo, Byeongmo
Jeon, Ho Sung
Hong, Sung Hyup
Rim, Donghyun
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  organization: Dept. of Architectural Engineering, Pennsylvania State Univ
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Cites_doi 10.1097/MEG.0b013e3282f198a0
10.1007/s12206-022-0241-4
10.1016/j.biombioe.2012.11.022
10.1016/j.seta.2021.101166
10.1016/j.energy.2023.126617
10.5772/15751
10.1016/j.energy.2022.125217
10.1016/j.eswa.2009.02.073
10.1016/j.egyr.2022.01.120
10.1016/j.solener.2016.06.069
10.1109/TSTE.2016.2613962
10.1016/j.enconman.2021.114847
10.1016/j.rineng.2023.101184
10.1016/j.solener.2020.08.045
10.1016/j.enbuild.2007.10.002
10.1155/2023/3525651
10.1016/j.egyr.2022.11.208
10.1016/j.eswa.2007.08.081
10.1016/j.enconman.2013.12.047
10.1109/PVSC.2018.8547794
10.1016/j.renene.2023.02.130
10.1007/s11356-019-05550-y
10.1016/j.ijthermalsci.2007.03.004
10.1016/j.rser.2017.08.017
10.3390/en11071767
10.1162/neco_a_01199
10.1016/j.egyr.2023.05.263
10.1016/j.rser.2010.09.011
10.2172/1236174
10.1016/j.epsr.2021.107596
10.1016/j.ifacol.2022.07.051
10.1016/j.solener.2023.05.037
10.5120/ijca2016910728
10.3390/en15197231
10.3390/buildings13061434
10.1213/ANE.0000000000002864
10.1016/j.enconman.2013.11.021
10.1016/j.apenergy.2023.121275
10.1016/j.renene.2021.05.095
10.1016/j.enbuild.2023.113471
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References AggaAAbbouALabbadiMEl HoumYShort-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM modelsRenewable Energy202117710111210.1016/j.renene.2021.05.095
DasU KTeyK SSeyedmahmoudianMMekhilefSIdrisM Y IVan DeventerWStojcevskiAForecasting of photovoltaic power generation and model optimization: a reviewRenewable and Sustainable Energy Reviews20188191292810.1016/j.rser.2017.08.017
JiH JYeonS HParkJYoonYLeeK HMachine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office buildingEnergy and Buildings202329711347110.1016/j.enbuild.2023.113471
J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed., L. Erlbaum Associates (1988).
SchoberPSchwarteL ACorrelation coefficients: appropriate use and interpretationAnesthesia and Analgesia20181261763176810.1213/ANE.0000000000002864
Korea Energy AgencyRequirement to Install Renewable Facilities in New, Expanded, or Renovated Public Buildings2020KoreaKorea Energy Agency
ASHRAE, ASHRAE Guideline 14-2014. Measurement of Energy, Demand and Water Savings, ASHRAE (2014).
Sinil Engineering & ConstructionClassification of Electricity/heat Energy Demand and Demand Characteristics Model Design in Smart Cities2019KoreaSinil Engineering & Construction
G. Masson, I. Kaizuka, J. Lindahl, A. JaegerWaldau, G. Neubourg, P. Ahm and F. Tilli, A snapshot of global PV markets-the latest survey results on PV markets and policies from the IEA PVPS programme in 2017, 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC)(A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), Waikoloa, HI, USA (2018) 3825–3828.
C. Olah, Understanding LSTM Networks, Colah’s Blog (2015) https://colah.github.io/posts/2015-08-Understanding-LSTMs/.
NetsanetSZhengDZhangWTeshagerGShort-term PV power forecasting using variational mode decomposition integrated with ant colony optimization and neural networkEnergy Reports202282022203510.1016/j.egyr.2022.01.120
SaifurrohmanM HHasyidM HPutrantoL MHadiS PSusatyoWIsnandarSBattery energy storage systems reinforcement control strategy to enhanced the maximum integration of PV to generation systemsResults in Engineering20231810118410.1016/j.rineng.2023.101184
A. Krenker, J. Bester and A. Kos, Introduction to the artificial neural networks, Artificial Neural Networks: Methodological Advances and Biomedical Applications, InTech (2011) 1–18.
BlaschkeTBiberacherMGadochaSSchardingerIEnergy landscapes: meeting energy demands and human aspirationsBiomass and Bioenergy20135531610.1016/j.biombioe.2012.11.022
AntonanzasJOsorioNEscobarRUrracaRMartinez-de-PisonF JAntonanzas-TorresFReview of photovoltaic power forecastingSolar Energy20161367811110.1016/j.solener.2016.06.069
LeeY JHongS HLeeJ MYoonYChoiJ MLeeK HChilled water temperature set-point reset based on outdoor air temperature and its cooling energy performance in an office buildingJ. of Mechanical Science and Technology2022361557156810.1007/s12206-022-0241-4
RajamandSShafie-khahMCatalãoJ PEnergy storage systems implementation and photovoltaic output prediction for cost minimization of a MicrogridElectric Power Systems Research202220210759610.1016/j.epsr.2021.107596
United Nations, Sustainable Urban Energy Is the Future, United Nations (2015) https://www.un.org/en/chronicle/article/sustainable-urban-energy-future.
GrossiEBuscemaMIntroduction to artificial neural networksEuropean J. of Gastroenterology & Hepatology2007191046105410.1097/MEG.0b013e3282f198a0
ZhangDChenYWangLLiuJYuanRWuJLiMControl strategy and optimal configuration of energy storage system for smoothing short-term fluctuation of PV powerSustainable Energy Technologies and Assessments20214510116610.1016/j.seta.2021.101166
ZahediAMaximizing solar PV energy penetration using energy storage technologyRenewable and Sustainable Energy Reviews20111586687010.1016/j.rser.2010.09.011
ChaturvediD KIshaISolar power forecasting: a reviewInternational J. of Computer Applications2016145285010.5120/ijca2016910728
KamaniDArdehaliM MLong-term forecast of electrical energy consumption with considerations for solar and wind energy sourcesEnergy202326812661710.1016/j.energy.2023.126617
ParkJHongS HYeonS HSeoB MLeeK HPredictive model for solar insolation using the deep learning techniqueInternational J. of Energy Research20232023352565110.1155/2023/3525651
KimDLeeJ MDoSMagoP JLeeK HChoHEnergy modeling and model predictive control for HVAC in buildings: a review of current research trendsEnergies202215723110.3390/en15197231
KurdiYAlkhatatbehB JAsadiSThe influence of electricity transaction models on the optimal design of PV and PV-BESS systemsSolar Energy202325943745110.1016/j.solener.2023.05.037
EsenHInalliMSengurAEsenMArtificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump systemEnergy and Buildings2008401074108310.1016/j.enbuild.2007.10.002
BermüdezJ MRuisánchezEArenillasAMorenoA HMenéndezJ ANew concept for energy storage: microwave-induced carbon gasification with CO2Energy Conversion and Management20147855956410.1016/j.enconman.2013.11.021
SeoBYoonYLeeK HChoSComparative analysis of ANN and LSTM prediction accuracy and cooling energy savings through AHU-DAT control in an office buildingBuildings202313143410.3390/buildings13061434
YuYSiXHuCZhangJA review of recurrent neural networks: LSTM cells and network architecturesNeural Computation20193112351270398846410.1162/neco_a_01199
EmadDEl-HameedM AEl-FerganyA AOptimal techno-economic design of hybrid PV/wind system comprising battery energy storage: case study for a remote areaEnergy Conversion and Management202124911484710.1016/j.enconman.2021.114847
RehmanARaufAAhmadMChandioA ADeyuanZThe effect of carbon dioxide emission and the consumption of electrical energy, fossil fuel energy, and renewable energy, on economic performance: evidence from PakistanEnvironmental Science and Pollution Research201926217602177310.1007/s11356-019-05550-y
TavaresIManfrediniRAlmeidaJSoaresJRamosSForoozandehZValeZComparison of PV power generation forecasting in a residential building using ANN and DNNIFAC-PapersOnLine20225529129610.1016/j.ifacol.2022.07.051
Scikit-learn, Compare the Effect of Different Scalers on Data with Outliers, Scikit-learn (2022) https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html.
EsenHInalliMSengurAEsenMPerformance prediction of a ground-coupled heat pump system using artificial neural networksExpert Systems with Applications20083541940194810.1016/j.eswa.2007.08.081
ISO 18523-2: 2018, Energy Performance of Buildings — Schedule and Condition of Building, Zone and Space Usage for Energy Calculation — Part 2: Residential Buildings, International Organization for Standardization (2018).
U.S. Department of EnergyInput Output Reference of EnergyPlus2023USAU.S. Department of Energy
YeonS HKangW HLeeJ HSongK WChaeY TLeeK HUpper and lower threshold limit of chilled and condenser water temperature set-points during ann based optimized controlEnergy Reports202396349636110.1016/j.egyr.2023.05.263
EsenHOzgenFEsenMSengurAArtificial neural network and wavelet neural network approaches for modelling of a solar air heaterExpert Systems with Applications200836112401124810.1016/j.eswa.2009.02.073
Korea Energy AgencyInstitutional Energy Storage System (ESS) Installation Guidelines2020KoreaKorea Energy Agency
do NascimentoA D JRütherREvaluating distributed photovoltaic (PV) generation to foster the adoption of energy storage systems (ESS) in time-of-use frameworksSolar Energy202020891792910.1016/j.solener.2020.08.045
AsrariAWuT XRamosBA hybrid algorithm for short-term solar power prediction—sunshine state case studyIEEE Transactions on Sustainable Energy2016858259110.1109/TSTE.2016.2613962
FengZ KHuangQ QNiuW JYangTWangJ YWenS PMulti-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithmEnergy202226112521710.1016/j.energy.2022.125217
HongS HLeeJ MMoonJ WLeeK HThermal comfort, energy and cost impacts of PMV control considering individual metabolic rate variations in residential buildingEnergies201811176710.3390/en11071767
BresterCKallio-MyersVLindforsA VKolehmainenMNiskaHEvaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observationsRenewable Energy202320726627410.1016/j.renene.2023.02.130
MaTYangHLuLFeasibility study and economic analysis of pumped hydro storage and battery storage for a renewable energy powered islandEnergy Conversion and Management20147938739710.1016/j.enconman.2013.12.047
ErdincF GRolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairnessApplied Energy202334412127510.1016/j.apenergy.2023.121275
EsenHInalliMSengurAEsenMForecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processingInternational J. of Thermal Sciences20084743144110.1016/j.ijthermalsci.2007.03.004
GrandersonJTouzaniSCustodioCSohnMFernandesSJumpDAssessment of Automated Measurement and Verification (M&V) Methods2015Berkeley, USALawrence Berkeley National Laboratory10.2172/1236174
AlcañizAGrzebykDZiarHIsabellaOTrends and gaps in photovoltaic power forecasting with machine learningEnergy Reports2023944747110.1016/j.egyr.2022.11.208
H Esen (739_CR27) 2008; 36
I Tavares (739_CR19) 2022; 55
739_CR47
H Esen (739_CR24) 2008; 40
A D J do Nascimento (739_CR13) 2020; 208
739_CR1
739_CR4
Z K Feng (739_CR23) 2022; 261
739_CR45
A Zahedi (739_CR8) 2011; 15
D Zhang (739_CR9) 2021; 45
739_CR41
D K Chaturvedi (739_CR15) 2016; 145
J Granderson (739_CR49) 2015
J M Bermüdez (739_CR7) 2014; 78
Y Kurdi (739_CR11) 2023; 259
D Kim (739_CR37) 2022; 15
D Kamani (739_CR21) 2023; 268
J Antonanzas (739_CR18) 2016; 136
739_CR50
H Esen (739_CR25) 2008; 47
H Esen (739_CR26) 2008; 35
Sinil Engineering & Construction (739_CR33) 2019
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S H Yeon (739_CR48) 2023; 9
Korea Energy Agency (739_CR30) 2020
T Ma (739_CR6) 2014; 79
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U.S. Department of Energy (739_CR46) 2023
A Rehman (739_CR2) 2019; 26
E Grossi (739_CR36) 2007; 19
B Seo (739_CR40) 2023; 13
A Asrari (739_CR29) 2016; 8
F G Erdinc (739_CR10) 2023; 344
C Brester (739_CR20) 2023; 207
A Agga (739_CR28) 2021; 177
S H Hong (739_CR42) 2018; 11
D Emad (739_CR12) 2021; 249
739_CR38
S Rajamand (739_CR17) 2022; 202
J Park (739_CR32) 2023; 2023
Korea Energy Agency (739_CR31) 2020
739_CR35
Y J Lee (739_CR43) 2022; 36
S Netsanet (739_CR22) 2022; 8
Y Yu (739_CR39) 2019; 31
P Schober (739_CR44) 2018; 126
U K Das (739_CR16) 2018; 81
M H Saifurrohman (739_CR14) 2023; 18
References_xml – volume: 19
  start-page: 1046
  year: 2007
  ident: 739_CR36
  publication-title: European J. of Gastroenterology & Hepatology
  doi: 10.1097/MEG.0b013e3282f198a0
  contributor:
    fullname: E Grossi
– volume: 36
  start-page: 1557
  year: 2022
  ident: 739_CR43
  publication-title: J. of Mechanical Science and Technology
  doi: 10.1007/s12206-022-0241-4
  contributor:
    fullname: Y J Lee
– volume: 55
  start-page: 3
  year: 2013
  ident: 739_CR3
  publication-title: Biomass and Bioenergy
  doi: 10.1016/j.biombioe.2012.11.022
  contributor:
    fullname: T Blaschke
– volume: 45
  start-page: 101166
  year: 2021
  ident: 739_CR9
  publication-title: Sustainable Energy Technologies and Assessments
  doi: 10.1016/j.seta.2021.101166
  contributor:
    fullname: D Zhang
– volume-title: Classification of Electricity/heat Energy Demand and Demand Characteristics Model Design in Smart Cities
  year: 2019
  ident: 739_CR33
  contributor:
    fullname: Sinil Engineering & Construction
– volume: 268
  start-page: 126617
  year: 2023
  ident: 739_CR21
  publication-title: Energy
  doi: 10.1016/j.energy.2023.126617
  contributor:
    fullname: D Kamani
– ident: 739_CR35
  doi: 10.5772/15751
– volume: 261
  start-page: 125217
  year: 2022
  ident: 739_CR23
  publication-title: Energy
  doi: 10.1016/j.energy.2022.125217
  contributor:
    fullname: Z K Feng
– volume: 36
  start-page: 11240
  year: 2008
  ident: 739_CR27
  publication-title: Expert Systems with Applications
  doi: 10.1016/j.eswa.2009.02.073
  contributor:
    fullname: H Esen
– volume: 8
  start-page: 2022
  year: 2022
  ident: 739_CR22
  publication-title: Energy Reports
  doi: 10.1016/j.egyr.2022.01.120
  contributor:
    fullname: S Netsanet
– volume: 136
  start-page: 78
  year: 2016
  ident: 739_CR18
  publication-title: Solar Energy
  doi: 10.1016/j.solener.2016.06.069
  contributor:
    fullname: J Antonanzas
– volume: 8
  start-page: 582
  year: 2016
  ident: 739_CR29
  publication-title: IEEE Transactions on Sustainable Energy
  doi: 10.1109/TSTE.2016.2613962
  contributor:
    fullname: A Asrari
– volume: 249
  start-page: 114847
  year: 2021
  ident: 739_CR12
  publication-title: Energy Conversion and Management
  doi: 10.1016/j.enconman.2021.114847
  contributor:
    fullname: D Emad
– volume: 18
  start-page: 101184
  year: 2023
  ident: 739_CR14
  publication-title: Results in Engineering
  doi: 10.1016/j.rineng.2023.101184
  contributor:
    fullname: M H Saifurrohman
– ident: 739_CR47
– volume: 208
  start-page: 917
  year: 2020
  ident: 739_CR13
  publication-title: Solar Energy
  doi: 10.1016/j.solener.2020.08.045
  contributor:
    fullname: A D J do Nascimento
– volume: 40
  start-page: 1074
  year: 2008
  ident: 739_CR24
  publication-title: Energy and Buildings
  doi: 10.1016/j.enbuild.2007.10.002
  contributor:
    fullname: H Esen
– volume: 2023
  start-page: 3525651
  year: 2023
  ident: 739_CR32
  publication-title: International J. of Energy Research
  doi: 10.1155/2023/3525651
  contributor:
    fullname: J Park
– volume: 9
  start-page: 447
  year: 2023
  ident: 739_CR5
  publication-title: Energy Reports
  doi: 10.1016/j.egyr.2022.11.208
  contributor:
    fullname: A Alcañiz
– volume: 35
  start-page: 1940
  issue: 4
  year: 2008
  ident: 739_CR26
  publication-title: Expert Systems with Applications
  doi: 10.1016/j.eswa.2007.08.081
  contributor:
    fullname: H Esen
– volume: 79
  start-page: 387
  year: 2014
  ident: 739_CR6
  publication-title: Energy Conversion and Management
  doi: 10.1016/j.enconman.2013.12.047
  contributor:
    fullname: T Ma
– ident: 739_CR4
  doi: 10.1109/PVSC.2018.8547794
– volume: 207
  start-page: 266
  year: 2023
  ident: 739_CR20
  publication-title: Renewable Energy
  doi: 10.1016/j.renene.2023.02.130
  contributor:
    fullname: C Brester
– volume: 26
  start-page: 21760
  year: 2019
  ident: 739_CR2
  publication-title: Environmental Science and Pollution Research
  doi: 10.1007/s11356-019-05550-y
  contributor:
    fullname: A Rehman
– volume: 47
  start-page: 431
  year: 2008
  ident: 739_CR25
  publication-title: International J. of Thermal Sciences
  doi: 10.1016/j.ijthermalsci.2007.03.004
  contributor:
    fullname: H Esen
– ident: 739_CR38
– volume: 81
  start-page: 912
  year: 2018
  ident: 739_CR16
  publication-title: Renewable and Sustainable Energy Reviews
  doi: 10.1016/j.rser.2017.08.017
  contributor:
    fullname: U K Das
– ident: 739_CR50
– volume: 11
  start-page: 1767
  year: 2018
  ident: 739_CR42
  publication-title: Energies
  doi: 10.3390/en11071767
  contributor:
    fullname: S H Hong
– volume-title: Institutional Energy Storage System (ESS) Installation Guidelines
  year: 2020
  ident: 739_CR30
  contributor:
    fullname: Korea Energy Agency
– volume: 31
  start-page: 1235
  year: 2019
  ident: 739_CR39
  publication-title: Neural Computation
  doi: 10.1162/neco_a_01199
  contributor:
    fullname: Y Yu
– volume: 9
  start-page: 6349
  year: 2023
  ident: 739_CR48
  publication-title: Energy Reports
  doi: 10.1016/j.egyr.2023.05.263
  contributor:
    fullname: S H Yeon
– volume: 15
  start-page: 866
  year: 2011
  ident: 739_CR8
  publication-title: Renewable and Sustainable Energy Reviews
  doi: 10.1016/j.rser.2010.09.011
  contributor:
    fullname: A Zahedi
– volume-title: Input Output Reference of EnergyPlus
  year: 2023
  ident: 739_CR46
  contributor:
    fullname: U.S. Department of Energy
– ident: 739_CR41
– volume-title: Assessment of Automated Measurement and Verification (M&V) Methods
  year: 2015
  ident: 739_CR49
  doi: 10.2172/1236174
  contributor:
    fullname: J Granderson
– ident: 739_CR45
– ident: 739_CR1
– volume: 202
  start-page: 107596
  year: 2022
  ident: 739_CR17
  publication-title: Electric Power Systems Research
  doi: 10.1016/j.epsr.2021.107596
  contributor:
    fullname: S Rajamand
– volume: 55
  start-page: 291
  year: 2022
  ident: 739_CR19
  publication-title: IFAC-PapersOnLine
  doi: 10.1016/j.ifacol.2022.07.051
  contributor:
    fullname: I Tavares
– volume: 259
  start-page: 437
  year: 2023
  ident: 739_CR11
  publication-title: Solar Energy
  doi: 10.1016/j.solener.2023.05.037
  contributor:
    fullname: Y Kurdi
– volume-title: Requirement to Install Renewable Facilities in New, Expanded, or Renovated Public Buildings
  year: 2020
  ident: 739_CR31
  contributor:
    fullname: Korea Energy Agency
– volume: 145
  start-page: 28
  year: 2016
  ident: 739_CR15
  publication-title: International J. of Computer Applications
  doi: 10.5120/ijca2016910728
  contributor:
    fullname: D K Chaturvedi
– volume: 15
  start-page: 7231
  year: 2022
  ident: 739_CR37
  publication-title: Energies
  doi: 10.3390/en15197231
  contributor:
    fullname: D Kim
– volume: 13
  start-page: 1434
  year: 2023
  ident: 739_CR40
  publication-title: Buildings
  doi: 10.3390/buildings13061434
  contributor:
    fullname: B Seo
– volume: 126
  start-page: 1763
  year: 2018
  ident: 739_CR44
  publication-title: Anesthesia and Analgesia
  doi: 10.1213/ANE.0000000000002864
  contributor:
    fullname: P Schober
– volume: 78
  start-page: 559
  year: 2014
  ident: 739_CR7
  publication-title: Energy Conversion and Management
  doi: 10.1016/j.enconman.2013.11.021
  contributor:
    fullname: J M Bermüdez
– volume: 344
  start-page: 121275
  year: 2023
  ident: 739_CR10
  publication-title: Applied Energy
  doi: 10.1016/j.apenergy.2023.121275
  contributor:
    fullname: F G Erdinc
– volume: 177
  start-page: 101
  year: 2021
  ident: 739_CR28
  publication-title: Renewable Energy
  doi: 10.1016/j.renene.2021.05.095
  contributor:
    fullname: A Agga
– volume: 297
  start-page: 113471
  year: 2023
  ident: 739_CR34
  publication-title: Energy and Buildings
  doi: 10.1016/j.enbuild.2023.113471
  contributor:
    fullname: H J Ji
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Snippet 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...
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SubjectTerms 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
Title Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions
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