Nonparametric Demand Forecasting and Detection of Energy Aware Consumers


To extend the reliability of the facility grid and cut back the chance of power provide failure, demand-facet management (DSM) is of central importance. During this paper, a nonparametric take a look at is applied to detect if the demand behavior of consumers is per time-of-day electricity tariff initiatives. The test is based on Afriat’s theorem in economics and has the unique feature that it provides necessary and sufficient conditions to detect if the price-demand behavior is consistent with utility maximization (i.e., the test detects demand-responsive consumers) without prior knowledge of the buyer’s utility perform. For customers that are tuned in to time-of-day pricing initiatives, a nonparametric learning algorithm is used to forecast power demands for unobserved electricity tariffs. The nonparametric learning algorithm will be utilized in anticipatory management structures in a DSM framework to attain power usage objectives. Real-world knowledge from Ontario’s power system and numerical examples illustrate the accuracy of the nonparametric test and nonparametric learning algorithm for forecasting consumer demand.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Nonparametric joint shape and feature priors for segmentation of Dendritic spines - 2016 ABSTRACT: Multimodal form density estimation is a difficult task in many biomedical image segmentation problems. Existing
PROJECT TITLE :Exact Nonparametric Inference for Component and System Lifetime Distributions Based on Joint SignaturesABSTRACT:Based on the observed lifetimes of two systems with shared elements, we have a tendency to construct
PROJECT TITLE :Proportional-Integral Stabilizing Control of a Class of MIMO Systems Subject to Nonparametric Uncertainties by Additive-State-Decomposition Dynamic Inversion DesignABSTRACT:Based mostly on the additive-state-decomposition
PROJECT TITLE :Nonparametric Estimation of Time-Varying Systems Using 2-D RegularizationABSTRACT:In this paper a nonparametric time-domain estimation methodology of linear time-varying systems from measured noisy knowledge is
PROJECT TITLE :Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian techniqueABSTRACT:This work presents a cognitive waveform choice mechanism for chaotic ultra-wideband multiple-input multiple-output (MIMO) radars.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry