Skip to main content

Advertisement

SpringerLink
Log in

A fog based load forecasting strategy based on multi-ensemble classification for smart grids

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Internet of things (IoT) improves the development and operation of smart electrical grids (SEGs). Overcoming the cloud challenges, 2-tier architecture is replaced by 3-tier one for including a fog computing tier that acts as a bridge between the IoT devices embedded in SEG and cloud. Load forecasting is an essential process for the electrical system operation and planning as it provides intelligence to energy management. This paper completes the electrical load forecasting (ELF) strategy introduced (Rabie et al. in Cluster Comput 22(1):241–270, 2019). ELF consists of two phases which are; (1) data pre-processing Phase (DP2) and (2) load prediction phase (LP2). Both phases can be performed in the cloud tier on the stored data which is sent from fogs to cloud at cloud servers. DP2 aims to perform feature selection and outlier rejection using data mining techniques. The main contribution of this paper focuses on LP2 providing multi-ensemble load prediction (MELP) method which can be learned by the filtered data from DP2 to give fast and accurate predictions. MELP can deal with big electrical data based on Map-Reduce method. It mainly consists of two levels which are; (1) local ensemble level (LEL) in map phase and (2) global ensemble level (GEL) in reduce phase. In LEL, the ensemble classification principle is applied at every device in map phase. In GEL, the perfect and final decision for load prediction is taken in reduce phase based on global judger (GJ) method from many local predictions which are the results of all devices in map phase. The conducted experimental results have shown that the proposed MELP outperforms recent prediction methods in terms of accuracy, precision, recall, F1-measure, and run time. It is concluded that the proposed MELP method can deal with big electrical data. It has a good impact in maximizing system reliability, resilience, and stability as it introduces fast and accurate load predictions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  • Afzal M, Ashraf SMA (2016) Genetic algorithm for outlier detection. Int J Comput Sci Inf Technol (IJCSIT) 7(2):833–835

    Google Scholar 

  • Al-Ayyoub M, Jararweh Y, Rabab’ah A, Aldwairi M (2017) Feature extraction and selection for Arabic tweets authorship authentication. J Ambient Intell Hum Comput 8(3):383–393

    Article  Google Scholar 

  • Alkhraisat H, Rashaideh H (2016) Dynamic inertia weight particle swarm optimization for solving nonogram puzzles. Int J Adv Comput Sci Appl (IJACSA) 7(10):277–280

    Google Scholar 

  • Atlam HF, Walters RJ, Wills GB (2018) Fog computing and the internet of things: a review. Big Data Cognit Comput 2(10):1–18

    Google Scholar 

  • Ayyad SM, Saleh AI, Labib LM (2019) Gene expression cancer classification using modified K-Nearest Neighbors technique. BioSystems 176:41–51

    Article  Google Scholar 

  • Barik RK, Dubey H, Mankodiya K, Sasane SA, Misra C (2019) GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis. J Ambient Intell Hum Comput 10(2):551–567

    Article  Google Scholar 

  • Bibri SE (2018) The IoT for smart sustainable cities of the future: an analytical framework for sensor-based big data applications for environmental sustainability. Sustain Cities Soc 38:230–253

    Article  Google Scholar 

  • Chen Y, Xiong J, Xu W, Zuo J (2018) A novel online incremental and decremental learning algorithm based on variable support vector machine. Cluster Comput. https://doi.org/10.1007/s10586-018-1772-4

    Article  Google Scholar 

  • Di Mauro M, Di Sarno C (2014) A framework for Internet data real-time processing: a machine-learning approach. In: Proceedings of the 2014 international carnahan conference on security technology (ICCST), Rome, Italy, pp 1–6

  • Elgarhy SM, Othman MM, Taha A, Hasanien HM (2018) Short term load forecasting using ANN technique. In: Proceedings of the 2017 nineteenth international middle east power systems conference (MEPCON), Cairo, Egypt, pp 1385–1394

  • Etaiwi W, Biltawi M, Naymat G (2017) Evaluation of classification algorithms for banking customer’s behavior under Apache Spark Data Processing System. Procedia Computer Science 113:559–564

    Article  Google Scholar 

  • Feng X, Li S, Yuan C, Zeng P, Sun Y (2018) Prediction of slope stability using naive bayes classifier. KSCE J Civ Eng 22(3):941–950

    Article  Google Scholar 

  • Fong S, Li J, Song W, Tian Y, Wong RK, Dey N (2018) Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. J Ambient Intell Hum Comput 9(4):1197–1221

    Article  Google Scholar 

  • Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) A new prediction model based on multi-block forecast engine in smart grid. J Ambient Intell Hum Comput 9(6):1873–1888

    Article  Google Scholar 

  • He Y, Qin Y, Wang S, Wang X, Wang C (2019) Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network. Appl Energy 233:565–575

    Article  Google Scholar 

  • Jaradat M, Jarrah M, Bousselham A, Jararweh Y, Al-Ayyouba M (2015) The internet of energy: smart sensor networks and big data management for smart grid. Procedia Comput Sci 56:592–597

    Article  Google Scholar 

  • Khan M, Han K, Karthik S (2018) Designing smart control systems based on internet of things and big data analytics. Wireless Pers Commun 99(4):1683–1697

    Article  Google Scholar 

  • Landset S, Khoshgoftaar TM, Richter AN, Hasanin T (2015) A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J Big Data 2(1):1–36

    Article  Google Scholar 

  • Li X, Wang K, Liu L, Xin J, Yang H, Gao C (2011) Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Eng 26:2085–2091

    Article  Google Scholar 

  • Li N, Zeng L, He Q, Shi Z (2012) Parallel implementation of apriori algorithm based on map-reduce. In: Proceedings of the 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Kyoto, Japan, pp 236–241

  • Mahajan A, Patil P (2016) Internet of things based residential power load forecasting. Int Res J Eng Technol (IRJET) 3(7):1362–1364

    Google Scholar 

  • Malik H, Shakshuki EM (2018) Performance evaluation of counter selection techniques to detect discontinuity in large-scale-systems. J Ambient Intell Hum Comput 9(1):43–59

    Article  Google Scholar 

  • Meng R, Rice SG, Wang J, Sun X (2018) A fusion steganographic algorithm based on faster R-CNN. Comput Mater Continua 55(1):1–16

    Google Scholar 

  • Mousavi SM, Harwood A, Karunasekera S, Maghrebi M (2018) Enhancing the quality of geometries of interest (GOIs) extracted from GPS trajectory data using spatio-temporal data aggregation and outlier detection. J Ambient Intell Hum Comput 9(1):173–186

    Article  Google Scholar 

  • Okay FY, Ozdemir S (2016) A fog computing based smart grid model. In: Proceedings of the 2016 international symposium on networks, computers and communications (ISNCC), Yasmine Hammamet, Tunisia, pp 1– 6

  • Ozger M, Cetinkaya O, Akan OB (2018) Energy harvesting cognitive radio networking for iot-enabled smart grid. Mob Netw Appl 23(4):956–966

    Article  Google Scholar 

  • Rabie AH, Saleh AI, Abo-Al-Ez KM (2015) A new strategy of load forecasting technique for smart grids. Int J Modern Trends Eng Res (IJMTER) 2(12):332–341

    Google Scholar 

  • Rabie AH, Ali SH, Ali HA, Saleh AI (2019) A fog based load forecasting strategy for smart grids using big electrical data. Cluster Comput 22(1):241–270

    Article  Google Scholar 

  • Rathee S, Kashyap A (2018) Adaptive–Miner: an efficient distributed association rule mining algorithm on Spark. J Big Data 5(1):1–17

    Article  Google Scholar 

  • Sajadfara N, Mab Y (2015) A hybrid cost estimation framework based on feature-oriented data mining approach. Adv Eng Inf 29(3):633–647

    Article  Google Scholar 

  • Saleh AI, Rabie AH, Abo-Al-Ez KM (2016) A data mining based load forecasting strategy for smart electrical grids. Adv Eng Inform 30(3):422–448

    Article  Google Scholar 

  • Torabi A, Mousavy SAK, Dashti V, Saeedi M, Yousefi N (2019) A new prediction model based on cascade NN for wind power prediction. Comput Econ 53(3):1219–1243

    Article  Google Scholar 

  • Tu Y, Lin Y, Wang J, Kim JU (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Continua 55(2):243–254

    Google Scholar 

  • Valavanis KP (2018) The entropy based approach to modeling and evaluating autonomy and intelligence of robotic systems. J Intell Rob Syst 91(1):7–22

    Article  Google Scholar 

  • Vimala S, Sharmili KC (2018) Prediction of loan risk using naive bayes and support vector machine. Int Conf Adv Comput Technol (ICACT) 4(2):110–113

    Google Scholar 

  • Wang XX, Ma LY (2014) A compact K nearest neighbor classification for power plant fault diagnosis. J Inf Hiding Multimedia Signal Proc 5(3):508–517

    Google Scholar 

  • Wang D, Sun Z (2015) Big data analysis and parallel load forecasting of electric power user Side. Proc Chin Soc Electr Eng (Proceed CSEE) 35(3):527–537

    Google Scholar 

  • Wang L, Guo C, Li Y, Du B, Guo S (2019) An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing. J Ambient Intell Hum Comput 10(3):1065–1079

    Article  Google Scholar 

  • Wu J, Cui Z, Chen Y, Kong D, Wang YG (2019) A new hybrid model to predict the electrical load in five states of Australia. Energy 166:598–609

    Article  Google Scholar 

  • Xiang L, Li Y, Hao W, Yang P, Shen X (2018) Reversible natural language watermarking using synonym substitution and arithmetic coding. Comput Mater Continua 55(3):541–559

    Google Scholar 

  • Xu M, Huang G, Zhang M, Cui P, Wang C (2018) Load forecasting research based on high performance intelligent data processing of power big data. In: Proceedings of the 2018 2nd international conference on algorithms, computing and systems (ICACS ‘18), Beijing, China, pp 55–60

  • Zahid M, Ahmed F, Javaid N, Abbasi R, Kazmi HZ, Javaid A, Bilal M, Akbar M, Ilahi M (2019) electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics 8(2):1–32

    Article  Google Scholar 

  • Zeng D, Dai Y, Li F, Sherratt RS, Wang J (2018) Adversarial learning for distant supervised relation extraction. Comput Mater Continua 55(1):121–136

    Google Scholar 

  • Zhang Y (2015) TOPSIS method based on entropy weight for supplier evaluation of power grid enterprise. In: Proceedings of the 2nd international conference on education reform and modern management, pp 334–337

  • Zhang P, Wu X, Wang X, Bi S (2015) Short-term load forecasting based on big data technologies. CSEE J Power Energy Syst 1(3):59–67

    Article  Google Scholar 

  • Zhang R, Xu Y, Dong ZY, Kong W, Wong KP (2016) A Composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts. In: Proceedings of the 2016 IEEE power and energy society general meeting (PESGM), Boston, MA, USA, pp 1–5

  • Zhang L, Shan L, Wang J (2017) Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion. Neural Comput Appl 28(9):2795–2808

    Article  Google Scholar 

  • Zhang J, Jin X, Sun J, Wang J, Sangaiah AK (2018a) Spatial and semantic convolutional features for robust visual object tracking. Multimedia Tools Appl 25:26. https://doi.org/10.1007/s11042-018-6562-8

    Article  Google Scholar 

  • Zhang S, Li X, Zong M, Zhu X, Wang R (2018b) Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774–1784

    Article  MathSciNet  Google Scholar 

  • Zhao H, Tang Z, Shi W, Wang Z (2017) Study of short-term load forecasting in big data environment. In: Proceedings of the 2017 29th Chinese control and decision conference (CCDC), Chongqing, China, pp 6673–6678

Download references

Author information

Authors and Affiliations

  1. Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Asmaa H. Rabie, Ahmed I. Saleh & Hesham A. Ali

  2. Communications and Electronics Engineering Department, Delta Higher Institute for Engineering &Technology, Mansoura, Egypt

    Shereen H. Ali

Authors
  1. Asmaa H. Rabie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shereen H. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed I. Saleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hesham A. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asmaa H. Rabie.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rabie, A.H., Ali, S.H., Saleh, A.I. et al. A fog based load forecasting strategy based on multi-ensemble classification for smart grids. J Ambient Intell Human Comput 11, 209–236 (2020). https://doi.org/10.1007/s12652-019-01299-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01299-x

Keywords

Search

Navigation