Overview of the Application of Artificial Intelligence in Energy Internet

doi:10.3969/j.issn.1000-7229.2019.05.008

Abstract

Abstract: The artificial intelligence technology has outstanding advantages in solving complex problems efficiently. Energy Internet， fusing energy with information technology， can provide consumers with flexible energy sharing services. Due to the intermittence and volatility of power generation by renewable energy， not only effective utilization of renewable energy requires more real-time information of energy supply and demand， but also the mode of energy supply and demand becomes more complex and variable. Recently， artificial intelligence technology has been widely used in system modeling， prediction， control， optimization， and other aspects in the field of energy Internet. This paper makes an overview on the current research outputs of the artificial intelligence technology in typical application scenarios in energy Internet. Besides， the related future development trend is also put forward.

Key words: energy Internet, artificial intelligence, deep learning, neural network, machine learning

CLC Number:

TM 727
TP 18

XIAO Zeqing， HUA Haochen， CAO Junwei. Overview of the Application of Artificial Intelligence in Energy Internet[J]. Electric Power Construction, 2019, 40(5): 63-70.

References

［1］ RIFKIN J. The third industrial revolution: How lateral power is transforming energy， the economy， and the world［M］. New York: Palgrave Macmillan， 2013: 31-46.
［2］ CAO J， HUA H， REN G. The SAGE encyclopedia of the Internet［M］. Newbury Park: Sage， 2018: 344-350.
［3］ CAO Y， QIANG L I， TAN Y， et al. A comprehensive review of energy Internet: Basic concept， operation and planning methods， and research prospects［J］. Journal of Modern Power Systems and Clean Energy， 2018， 6（3）: 1-13.
［4］ YAN M， LI X， LAI L L， et al. Energy internet in smart city review［C］// International Conference on Wavelet Analysis and Pattern Recognition. Ningbo: IEEE， 2017:188-193.
［5］ HUA H， CAO J， YANG G， et al. Voltage control for uncertain stochastic nonlinear system with application to energy Internet: Non-fragile robust approach［J］. Journal of Mathematical Analysis and Applications， 2018， 463（1）: 93-110.
［6］ LIU Z， LUO Y， ZHUO R， et al. Distributed reinforcement learning to coordinate current sharing and voltage restoration for islanded DC microgrid［J］. Journal of Modern Power Systems and Clean Energy， 2017， 6（2）: 364-374.
［7］ YANG G， CAO J， HUA H， et al. Deep learning based distributed optimal control for wide area energy Internet［C］//Proceedings of the 2018 IEEE International Conference on Energy Internet. Beijing：IEEE， 2018: 292-297.
［8］ BOSE B K. Artificial intelligence techniques in smart grid and renewable energy systems: Some example applications［J］. Proceedings of the IEEE， 2017， 105（11）: 2262-2273.
［9］ ISMAIL M M， BENDARY A F. Smart battery controller using ANFIS for three phase grid connected PV array system［J/OL］. Mathematics and Computers in Simulation.［2018-11-11］.DOI: https://doi.org/10.1016/j.matcom.2018.04.008.
［10］ CONTEH F， TOBARU S， LOTFY M E， et al. An effective load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system［J］. Aims Energy， 2017， 5（5）: 814-837.
［11］ BAGHAEE H R， MIRSALIM M， GHAREHPETIAN G B， et al. Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles［J］. Simulation Modelling Practice and Theory， 2017， 72（C）: 51-68.
［12］ CABRERA P， CARTA J A， GONZALEZ J， et al. Wind-driven SWRO desalination prototype with and without batteries: A performance simulation using machine learning models［J］. Desalination， 2017， 435（1）: 77-96.
［13］ VINAYAGAM A， ALQUMSAN A A， SWARNA K S V， et al. Intelligent control strategy in the islanded network of a solar PV microgrid［J］. Electric Power Systems Research， 2018， 155: 93-103.
［14］ HUA H， HAO C， QIN Y， et al. A class of control strategies for energy Internet considering system robustness and operation cost optimization［J］. Energies， 2018， 11: 1593.
［15］ SOLIMAN M A， HASANIEN H M， AZAZI H Z， et al. Hybrid ANFIS-GA-based control scheme for performance enhancement of a grid-connected wind generator［J］. IET Renewable Power Generation， 2018， 12（7）: 832-843.
［16］ TIAN C， MA J， ZHANG C， et al. A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network［J］. Energies， 2018， 11: 3493.
［17］ SHI H， XU M， LI R. Deep learning for household load forecasting-a novel pooling deep RNN［J］. IEEE Transactions on Smart Grid， 2017， 9（5）: 5271-5280.
［18］ SUNDARAVELPANDIAN S， JOHAN S， PHILIPP G. Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction［J］. Advanced Engineering Informatics， 2018， 38: 81-90.
［19］ LI C， DING Z， ZHAO D， et al. Building energy consumption prediction: An extreme deep learning approach［J］. Energies， 2017， 10:1525.
［20］TOMIN N， KURBATSKY V， NEGNEVITSKY M. The concept of the deep learning-based system “artificial dispatcher” to power system control and dispatch［EB/OL］. ［2018-11-11］. https://arxiv.org/abs/1805.05408.
［21］ QIN Y， HUA H， CAO J. Stochastic optimal control scheme for battery lifetime extension in islanded microgrid via a novel modeling approach［J/OL］. IEEE Transactions on Smart Grid.［2018-11-11］.DOI: 10.1109/TSG.2018.2861221.
［22］ CHETTIBI N， MELLIT A， SULLIGOI G， et al. Adaptive neural network-based control of a hybrid AC/DC microgrid［J］. IEEE Transactions on Smart Grid， 2018， 9（3）: 1667-1679.
［23］ OMID R， RAHBARI， MAJID V， et al. Optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids［J］. Energy， 2017， 134:1053-1067.
［24］ HAO C， HUA H， QIN Y， et al. A class of optimal and robust controller design for energy routers in energy Internet［C］//Proceedings of the IEEE International Conference Smart Energy Grid Engineering. Oshawa: IEEE， 2018: 14-19.
［25］ HE Y， MENDIS G J， WEI J. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism［J］. IEEE Transactions on Smart Grid， 2017， 8（5）: 2505-2516.
［26］NIU X， SUN J. Dynamic detection of false data injection attack in smart grid using deep learning［EB/OL］.［2018-11-11］. https://arxiv.org/abs/1808.01094.
［27］ KONG X， XU X， YAN Z， et al. Deep learning hybrid method for islanding detection in distributed generation［J］. Applied Energy， 2017， 210: 776-785.
［28］ LOPEZ K L， GAGNE C， GARDNER M A. Demand-side management using deep learning for smart charging of electric vehicles［J/OL］. IEEE Transactions on Smart Grid.［2018-11-11］.DOI: 10.1109/TSG.2018.2808247.
［29］ TORNAI K， OLAH A， DRENYOVSZKI R， et al. Deep learning based consumer classification for smart grid［M］. Smart Grid Inspired Future Technologies. Germany: Springer， 2017: 132-141.
［30］ JOSHI P A， PATEL J J. Computational analysis and intelligent control of load forecasting using time series method［C］//Proceedings of the International Conference on Intelligent Systems and Signal Processing .Gujarat: Springer， 2018: 297-306.
［31］ KHAN M， JAVAID N， IQBAL M N， et al. Load prediction based on multivariate time series forecasting for energy consumption and behavioral analytics［C］//Proceedings of the Conference on Complex， Intelligent， and Software Intensive Systems. Matsue: Springer， 2018: 305-316.
［32］李娜. 基于相似日选取的马尔科夫短期负荷预测方法［J］. 电工技术， 2018 （4）: 86-88.
LI Na. Short-term power load forecasting based on similar days and Markov［J］. Electric Engineering， 2018（4）: 86-88.
［33］LI X， LI C， CONG L， et al. Short-term load forecasting based on dynamic weight similar day selection algorithm［J］. Power System Protection & Control， 2017， 45（6）:1-8.
［34］张炀，汪洋，祝宇翔，等. 基于PAM和ELM的电力短期负荷预测相似日选取算法［J］. 电力大数据， 2017，20（12）: 84-87.
ZHANG Yang， WANG Yang， ZHU Yuxiang， et al. Similarity day selection algorithm for power short-term load forecasting［J］. Power Systems and Big Data， 2017，20（12）: 84-87.
［35］ YILDIZ B， BILBAO J I， SPROUL A B. A review and analysis of regression and machine learning models on commercial building electricity load forecasting［J］. Renewable and Sustainable Energy Reviews， 2017， 73: 1104-1122.
［36］ SABER A Y， KHANDELWAL T. IoT based online load forecasting［C］//Proceedings of the Green Technologies Conference. IEEE， 2017: 189-194.
［37］ ZHANG W， HUA H， CAO J. Short term load forecasting based on IGSA-ELM algorithm［C］// Proceedings of the 2017 IEEE International Conference on Energy Internet. Beijing: IEEE， 2017: 296-301.
［38］ MOCANU E， MOCANU D C， NGUYEN P H， et al. On-line building energy optimization using deep reinforcement learning［J/OL］. IEEE Transactions on Smart Grid.［2018-11-11］.DOI: 10.1109/TSG.2018.2834219.
［39］ JOZI A， PINTO T， PRACA I， et al. Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study［C］// Proceedings of the Computational Intelligence. Honolulu: IEEE， 2018: 1-5.
［40］彭钟华. 基于遗传算法优化PNN的短期负荷预测［J］. 电气开关， 2017， 55（1）: 49-51， 56.
PENG Zhonghua. Short-term load forecasting based on PNN optimized by genetic algorithm［J］. Electric Switchgear， 2017， 55（1）: 49-51， 56.
［41］ KUMAR H， SAINI S. Chaotic characterization of electric load demand time series & load forecasting by using GA trained artificial neural network［C］// Proceedings of the International Conference on Signal Processing， Communication， Power and Embedded System. Paralakhemundi: IEEE， 2017: 1423-1428.
［42］董明亮，刘培胜，潘振，等. 基于SVM-GA模型的城市天然气长期负荷预测［J］. 辽宁石油化工大学学报， 2017， 37（2）: 31-36.
DONG Mingliang， LIU Peisheng， PAN Zhen， et al. A forecasting model of natural gas long-term load based on SVM-GA［J］. Journal of Liaoning Shihua University， 2017， 37（2）: 31-36.
［43］ HUO J， SHI T， CHANG J. Comparison of random forest and SVM for electrical short-term load forecast with different data sources［C］//Proceedings of the IEEE International Conference on Software Engineering and Service Science. Beijing: IEEE， 2017: 1077-1080.
［44］梁智，孙国强，李虎成，等. 基于VMD与PSO优化深度信念网络的短期负荷预测［J］. 电网技术， 2018， 42（2）: 598-606.
LIANG Zhi， SUN Guoqiang， LI Hucheng， et al. Short-term load forecasting based on VMD and PSO optimized deep belief network［J］. Power System Technology， 2018， 42（2）: 598-606.
［45］ YOUSSEF A， EL-TELBANY M， ZEKRY A. The role of artificial intelligence in photo-voltaic systems design and control: A review［J］. Renewable and Sustainable Energy Reviews， 2017， 78: 72-79.
［46］ HUANG N， LI R， LIN L， et al. Low redundancy feature selection of short term solar irradiance prediction using conditional mutual information and gauss process regression［J］. Sustainability， 2018， 10（8）: 2889.
［47］ QING X， NIU Y. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM［J］. Energy， 2018， 148: 461-468.
［48］SANSA I， BELLAAJ N M. Advanced applications for artificial neural networks ［M］. London：IntechOpen Limited，2018.
［49］ MEENAL R， SELVAKUMAR A I. Assessment of SVM， empirical and ANN based solar radiation prediction models with most influencing input parameters［J］. Renewable Energy， 2018， 121: 324-343.
［50］BENMOUIZA K， CHEKNANE A. Clustered ANFIS network using fuzzy c-means， subtractive clustering， and grid partitioning for hourly solar radiation forecasting［J/OL］. Theoretical and Applied Climatology.［2018-11-11］.DOI: https://doi.org/ 10.1007/s00704-018-2576-4.
［51］KERBOUCHE K， HADDAD S， RABHI A， et al. A GRNN based algorithm for output power prediction of a PV panel［C］//Proceedings of 2017 International Conference on Artificial Intelligence in Renewable Energetic Systems. Tipaza: Springer， 2018: 291-298.
［52］LI L L， WEN S Y， TSENG M L， et al. Photovoltaic array prediction on short-term output power method in centralized power generation system［J/OL］. Annals of Operations Research.［2018-11-11］.DOI: https://doi.org/10.1007/s10479-018- 2879-y.
［53］ZHANG W， DANG H， SIMOES R. A new solar power output prediction based on hybrid forecast engine and decomposition model［J］. Isa Transactions， 2018， 81: 105-120.
［54］NETSANET S， ZHANG J， ZHENG D， et al. An aggregative machine learning approach for output power prediction of wind turbines［C］//Proceedings of IEEE Texas Power and Energy Conference. Texas: IEEE， 2018: 1-6.
［55］BILAL B O， ADJALLAH K H， SAVA A， et al. Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data［C］//Proceedings of IEEE International Conference on Industrial Technology. Lyon: IEEE， 2018: 1085-1092.
［56］SIRJANI R. Optimal placement and sizing of PV-STATCOM in power systems using empirical data and adaptive particle swarm optimization［J］. Sustainability， 2018， 10（3）:727.
［57］HUA H， QIN Y， HAO C， et al. Stochastic optimal control for energy Internet: A bottom-up energy management approach［J］. IEEE Transactions on Industrial Informatics，2019，15（3）:1788-1797.
［58］FARAYOLA A M， HASAN A N， ALI A. Optimization of PV systems using data mining and regression learner MPPT techniques［J］. Telkomnika Indonesian Journal of Electrical Engineering， 2018， 10（3）: 1080-1089.
［59］MEGANTORO P， WIJAYA F D， FIRMANSYAH E. Analyze and optimization of genetic algorithm implemented on maximum power point tracking technique for PV system［C］// Proceedings of International Seminar on Application for Technology of Information and Communication. Semarang: IEEE， 2018:79-84.
［60］GUO L， MENG Z， SUN Y， et al. A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition［J］. Energy， 2018， 144: 501-514.
［61］ASCIONE F， BIANCO N， STASIO C D， et al. CASA， cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit， feasible for any building［J］. Energy and Buildings， 2017， 146: 200-219.
［62］PARVIZIMOSAED M， FARMANI F， MONSEF H， et al. A multi-stage smart energy management system under multiple uncertainties: A data mining approach［J］. Renewable Energy， 2017， 102: 178-189.
［63］RICCIETTI E， BELLAVIA S， SELLO S. Sequential linear programming and particle swarm optimization for the optimization of energy districts［J］. Engineering Optimization， 2018， 51（1）: 84-100.
［64］LEE H S， TEKIN C， SCHAAR M V D， et al. Adaptive contextual learning for unit commitment in microgrids with renewable energy sources［J］. IEEE Journal of Selected Topics in Signal Processing， 2018， 12（4）: 688-702.
［65］ZHAO Q， SHAO S， LU L， et al. A new PV array fault diagnosis method using fuzzy C-mean clustering and fuzzy membership algorithm［J］. Energies， 2018， 11:238.
［66］LIVERA A， THERISTIS M， MAKRIDES G， et al. On-line failure diagnosis of grid-connected photovoltaic systems based on fuzzy logic［C］//Proceedings of the International Conference on Compatibility， Power Electronics and Power Engineering. Doha: IEEE， 2018：739-744.
［67］WANG M， QU Z， HE X， et al. Real time fault monitoring and diagnosis method for power grid monitoring and its application［C］//Proceedings of 2017 IEEE Conference on Energy Internet and Energy System Integration. Beijing: IEEE， 2018：1-6.
［68］WU C M， GONG H Q， YANG J H， et al. An improved FOA to optimize GRNN method for wind turbine fault diagnosis［J］. Journal of Information Hiding and Multimedia Signal Processing， 2018， 9（1）: 1-10.
［69］ZHAO H， GAO Y， LIU H， et al. Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine［J］. Journal of Modern Power Systems and Clean Energy， 2019， 7（2）: 350-356.
［70］HUANG J C， ZHOU D Y， JING X Y， et al. Research of fault diagnosis method for a electric component based on immune neural network and Pspice［C］// Proceedings of Guidance， Navigation and Control Conference. IEEE， 2017:227-231.
［71］KARIM M A， CURRIE J， LIE T T. Dynamic event detection using a distributed feature selection based machine learning approach in a self-healing microgrid［J］. IEEE Transactions on Power Systems， 2018， 33（5）: 4706-4718.
［72］PANIGRAHI B K， RAY P K， ROUT P K， et al. Detection and classification of faults in a microgrid using wavelet neural network［J］. Journal of Information and Optimization Sciences， 2017， 39（1）: 327-335.
［73］TELFORD R， GALLOWAY S， STEPHEN B， et al. Diagnosis of series DC arc faults: A machine learning approach［J］. IEEE Transactions on Industrial Informatics， 2017， 13（4）: 1598-1609.
［74］FOROUTAN S A， SALMASI F R. Detection of false data injection attacks against state estimation in smart grids based on a mixture Gaussian distribution learning method［J］. IET Cyber-Physical Systems: Theory and Applications， 2018， 2（4）: 161-171.
［75］IVKOVIC N， SARIC A T. Detection of false data injection attacks using unscented Kalman filter［J］. Journal of Modern Power Systems and Clean Energy， 2018， 6（5）: 847-859.
［76］OZAY M， ESNAOLA I， VURAL F T Y， et al. Machine learning methods for attack detection in the smart grid［J］. IEEE Transactions on Neural Networks and Learning Systems， 2017， 27（8）: 1773-1786.
［77］MONYEI C G， ADEWUMI A O. Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads- South Africa in focus［J］. Electric Power Systems Research， 2018， 158: 92-104.
［78］LI D， SUN H， CHIU W Y， et al. Multiobjective optimization for demand side management in smart grid［J］. IEEE Transactions on Industrial Informatics， 2018， 14（4）: 1482-1490.
［79］WANG K， LI H， MAHARJAN S， et al. Green energy scheduling for demand side management in the smart grid［J］. IEEE Transactions on Green Communications and Networking， 2018， 2（2）: 596-611.
［80］LIZONDO D， RODRIGUEZ S， WILL A， et al. An artificial immune network for distributed demand-side management in smart grids［J］. Information Sciences， 2018， 438:32-45.
［81］SANTO K G D， SANTO S G D， MONARO R M， et al. Active demand side management for households in smart grids using optimization and artificial intelligence［J］. Measurement， 2017， 115：152-161.
［82］KHAN N， JAVAID N， KHAN M S，et al. Harmony pigeon inspired optimization for appliance scheduling in smart grid［C］// 2018 Advanced Information Networking And Applications. AINA，2018: 1060-1069.
［83］ABDOLRASOL M G M， HANNAN M A， MOHAMED A， et al. An optimal scheduling controller for virtual power plant and microgrid integration using binary backtracking search algorithm［J］. IEEE Transactions on Industry Applications， 2018， 54（3）: 2834-2844.
［84］ZHOU S， LI X， DU N， et al. A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost［J］. Computers and Operations Research， 2018， 96: 55-68.
［85］CHEN X， XU B， DU W. An improved particle swarm optimization with biogeography-based learning strategy for economic dispatch problems［J/OL］. Complexity.［2018-11-11］.DOI: 10.1155/2018/7289674.
［86］ZENG P， LI H， HE H， et al. Dynamic energy management of a microgrid using approximate dynamic programming and deep recurrent neural network learning［J/OL］. IEEE Transactions on Smart Grid.［2018-11-11］.DOI: 10.1109/TSG.2018.2859821.
［87］GAJOWNICZEK K， NAFKHA R， ZABKOWSKI T. Electricity peak demand classification with artificial neural networks［C］// Proceedings of 2017 Federated Conference on Computer Science and Information Systems. Prague: IEEE， 2017: 307-315.
［88］BISOI R， DASH P K， DAS P P. Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine［J/OL］. Neural Computing and Applications.［2018-11-11］.DOI: 10.1007/s00521-018-3652-5.
［89］CAO J， ZHANG W， XIAO Z， et al. Reactive power optimization for transient voltage stability in energy Internet via deep reinforcement learning approach［J］. Energies，2019，12：1556.

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