IJSREG Trion Studio
IJSREG

No Publication Cost
Vol 7, No 3 :

subscription

Revealing Household Electricity Power Consumption Using Data Mining Algorithms
Raja , P. Arumugam , P. Jose
Abstract

Data mining algorithms are widely applied in many fields such us to predict energy consumption. Supervised learning algorithm has very good ability in the energy consumption prediction.  Feature Selection focused at selection of most relevant feature that establishes a good predictor for the concerned learning algorithm. This work proposes to investigate the viability of predicting residence detecting behavior by electricity consumption data. In this data set the most influenced attribute place a major role in prediction. In this paper we aim to evaluate the overall performance of a forecasting model that is a weather-free model created the usage of a database containing relevant records about past produced electricity records and data mining techniques. Data mining techniques had been used to increase the forecast models, particularly, Artificial neural Network and SVM. The results were used to confirm the fashions and select the nice one.
Full Text
PDF
References
. Dolara;F. Grimaccia; S. Leva;M. Mussetta; E. Ogliari; A physical hybrid artificial neural network for short term forecasting of PV plant power output, Energies 8 1138–1153 (2018).
A. Gandelli; F. Grimaccia; S. Leva; M. Mussetta; E. Ogliari; Hybrid model analysis and validation for PV energy production forecasting, 2014 International Joint Conference on Neutral Networks, 1957–1962 (2014).
A. I. Saleh; A. H. Rabie; K. M. Abo-Al-Ez; A data mining based load forecasting strategy for smart electrical grids, Advanced Engineering Informatics, 30 422–448 (2016).
A. Saberian; H. Hizam; M. A. M. Razid;M. Z. A. A. Kadir; M. Mirzaei; Modelling and prediction of photovoltaic power output using artificial neural networks. International Journal ofPhotoenergy, 14 1–10 (2014)
C. Wan; J. Zhao; Y. Song; Z. Xu;J. Lin; Z. Hu Z; Photovoltaic and solar power forecasting for smart grid energy management, CSEE Journal of Power and Energy Systems, 1 38–46 (2015).
D. L. Marino; K. Amarasinghe; M. Manic; Building energy load forecasting using deep neural networks, IECON 2016-42nd Ann Conf IEEE Ind Electron Soc:7046–7051. abs/1610.09460:1-6 (2016).
G. Chicco; Overview and performance assessment of the clustering methods for electrical load pattern grouping, Energy, 421 68–80 (2012).
G. Chicco; R. Napoli.; F. Piglione; P. Postolache; M. Scutariu; C. Toader; Load pattern-based classification of electricity customers, IEEE Transaction on Power System, 192 1232–1239 (2004).
I.P. Panapakidis; A. S, Bouhouras; G.C. Christoforidis; A missing data treatment method for photovoltaic installations, 2018 IEEE International Energy Conference (ENERGYCON), 1–6 (2018).
J. A. G. Ordiano; S. Waczowicz; M. Reischl; R. Mikut; V. Hagenmeyer; Photovoltaic power forecasting using simple data-driven models without weather data, Computer Science Researchand Development, 32 237–246 (2017).
J. Dobschinski; R. Bessa; P. Du; K. leiser; S. E. Haupt;M. Lange; C.Mhrlen; D. Nakafuji; M. Rodriguez Uncertainty forecasting in a nutshell: prediction models designed to prevent significant errors, IEEE Power and Energy Magazine, 15 40–49(2017).
J. Liu; W. Fang; X. Zhang; C. Yang; An improved photovoltaic power forecasting model with the assistance of aerosol index data, IEEE Transaction Sustainable Energy, 6 434–442 (2015).
J. Zhang; A. Florita; B. Hodge; S. Lu; H.F. Hamann; V. Banunarayan; A. M. Brockway; A suite of metrics for assessing the performance of solar power forecasting, Solar Energy,111 157–175 (2015).
K. Gajowniczek; T. Zabkowski; Short term electricity forecasting based on user behavior using individual smart meter data,Journal of Intelligent & Fuzzy System, 30 223–234 (2015).
L. Luo; T. Hong; M. Yue; Real-time anomaly detection for very short-term load forecasting, Journal of Modern Power Systemand Clean Energy, 6 235–243 (2018).
L. Suganthi; A. A. Samuel; Energy models for demand forecasting a review, Renewable Sustainable Energy Reviews, 16 1223–1240 (2012)
M. Alanazi; A. Alanazi; A. Khodaei; Long-term solar generation forecasting, 2016 IEEE/PES Trans DistribConf Expo (T & D):1–13 (2017).
P. Ramsami; V. Oree; A hybrid method for forecasting the energy output of photovoltaic systems, Energy Conversion and Management, 95 406–413 (2015).
S. Daliento;A. Chouder; P. Guerriero; A.M.Pavan; A. Mellit; R. Moeini; P. Tricoli; Monitoring, diagnosis and power forecasting for photovoltaic fields: a review, International Journal of Photoenergy, 1–13 (2017).
S. Haben; C. Singleton; P. Grindrod; Analysis and clustering of residential customers energy behavioral demand using smart meter data, IEEE Transaction on Smart Grid, 7 136–144 (2016).
S. Pelland; J. Remund; J. Kleissl; T. Oozeki; K. D. Brabandere; Photovoltaic and solar forecasting: State of the art. Tech Rep IEA PVPS:T14–T01, 1–36 (2013).
T. Khatib; W. Elmenreich W; A model for hourly solar radiation data generation from daily solar radiation data using a generalized regression artificial neural network, International Journal of Photoenergy, 1–13 (2015).
U. B. Filik; O. N. Gerek; M. Kurban M; Hourly forecasting of long term electric energy demand using novel mathematical models and neural networks,International Journal of Innovative computing, Information and Control, l 7 115–118 (2011).
X. Chen; C. Kang; X. Tong; Q. Xia; J. Yang; Improving the accuracy of bus load forecasting by two-stage bad data identification method, IEEE Transaction of Power System, 29 1634–1641 (2014).
Y. Chakhchoukh; P. Panciatici; L. Mili; Electric load forecasting based on statistical robust method, IEEE Transaction on Power System, 26 982–991 (2011)

ISSN(P) 2350-0174

ISSN(O) 2456-2378

Journal Content
Browser