ANN and fuzzy based household energy consumption prediction with high accuracy
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ORIGINAL RESEARCH
ANN and fuzzy based household energy consumption prediction with high accuracy K. Balachander1 · D. Paulraj2 Received: 16 May 2020 / Accepted: 1 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Timeline Data is gathered according to different time intervals, which are day after day after week or month after month, for updating properties and rationing institutional resilience it is important to consider the usage of systems and lead to reduced lifespan Such details illustrate the use of the system as well as its interaction with time, like day, week, month and time of year, and the relation between the equipment and a relative, essential factors for the effects of the usage of their potency and the expected movement by customers. This is because it is not significant to determine the various relations between specific devices utilizing concurrent data. In addition, precise relations between interval-based instances in which specific system usage continue for certain duration cannot be calculated. To address these difficulties, we propose supervised energy time series data clustering and frequent pattern mining analysis as well as a Bayesian network forecast for energy use. However, the AI model is a univariate construct based on past use-values. Neural Networks have the favored position that can estimate nonlinear limits. Everything together they have an approximate usage of vitality, the ANN adds in a planning knowledge table between the use of vitality (EC) and its determinants. SVM is capable of reliably calculating knowledge on time structure while the basic system mechanism is frequently nonlinear and not set. Also, certain nonlinear mechanisms such as multilayer perceptron have been shown to flank SVM. The single data has been converted into a multivariate and the ANFIS has been selected as it transmits both the AI (ANN) and Fuzzy Inference Method (FIS) points of concern. ANFIS yields the accuracy, RMSE, and MAPE among genuine and anticipated power utilization of 91.19%, 0.4076 and 0.9049 which is moderately low. Keywords ARIMA · Fuzzy time series · ANN · SVR · Energy consumption
1 Introduction Power is basic in society, regardless of whether it is for home use or in the different sections of the economy. Routine exercises, for example, sitting in front of the TV or utilizing the climate control system are just conceivable through the electric burden. The power load section gets a lot of energy from the transmission framework and disperses it to the last customers, homes, independent ventures, and businesses. The power load supply is one of the most testing * K. Balachander [email protected] D. Paulraj [email protected] 1
Department of CSE, Velammal Institute of Technology, Pancheti, Tamilnadu, India
Department of CSE, R.M.D.Engineering College, Kavarapettai, Tamilnadu, India
2
administrations of current society. A political or monetary transition is taking place in the energy segment around the globe (Amara et al. 2019). Box an
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