Determining the load profiles of consumers based on fuzzy logic and probability neural networks

Gerbec, D.; Gasperic, S.; Smon, I.; Gubina, F.

doi:10.1049/ip-gtd:20040472

Cited by 76 publications

(38 citation statements)

References 10 publications

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“…On the other hand in [23][24], Probabilistic Neural Networks (PNNs) and Fuzzy C Means (FCM) clustering algorithm were used to determine and allocate the typical LPs to consumers. Unsupervised learning based on SOMs have been used in [25][26][27] to classify, filter and identify customers' consumption patterns in order to learn both their distribution and topology, and segment the demand patterns for electrical customers.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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“…Top-down approaches require the implementation of econometric and technological data in a macroscopic level. Bottom-up ones correspond to the cognitive approaches (see [21] for a comprehensive review of existing implementations), where methods such as neural networks [12], fuzzy logic [8], conditional demand analysis and regression techniques [3] are used along with past consumption data, in order to predict future load profiles. The problem, in the latter case, is that cognitive systems are heavily dependent on the nature of the past data used for their training procedure.…”

Section: Energy Efficiency Frameworkmentioning

confidence: 99%

CASSANDRA - A simulation-based, decision-support tool for energy market stakeholders

Vavliakis¹,

Chrysopoulos²,

Chatzidimitriou³

et al. 2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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Energy gives personal comfort to people, and is essential for the generation of commercial and societal wealth. Nevertheless, energy production and consumption place considerable pressures on the environment, such as the emission of greenhouse gases and air pollutants. They contribute to climate change, damage natural ecosystems and the man-made environment, and cause adverse effects to human health. Lately, novel market schemes emerge, such as the formation and operation of customer coalitions aiming to improve their market power through the pursuit of common benefits.In this paper we present CASSANDRA, an open source 1 , expandable software platform for modelling the demand side of power systems, focusing on small scale consumers. The structural elements of the platform are a) the electrical installations (i.e. households, commercial stores, small industries etc.), b) the respective appliances installed, and c) the electrical consumption-related activities of the people residing in the installations.CASSANDRA serves as a tool for simulation of real demandside environments providing decision support for energy market stakeholders. The ultimate goal of the CASSANDRA 1 https://github.com/cassandra-project simulation functionality is the identification of good practices that lead to energy efficiency, clustering electric energy consumers according to their consumption patterns, and the studying consumer change behaviour when presented with various demand response programs.

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“…Methods to address this problem consist of statistical methods and artificial intelligence based techniques. Artificial intelligence methods include fuzzy logic approaches [13], neural networks [14], expert systems [15], and pattern-recognition techniques [16][17][18]. Statistical methods include linear regression, exponential smoothing [19], stochastic time series [20], and state space models [21].…”

Section: Introductionmentioning

confidence: 99%

Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

Yang

Ren

et al. 2018

Mathematical Problems in Engineering

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Under the application scenario of smoothing photovoltaic (PV) power fluctuation, a novel typical daily power curve mining method is developed for a battery energy storage system (BESS) that utilizes the power probability distribution and Bloch spherical quantum genetic algorithm. The charging/discharging of BESS is analyzed by applying fuzzy-means clustering techniques. In the mining approach, at any sample time, those distribution intervals containing concentrated power points are individually located by using probability distribution information and Bloch spherical quantum genetic algorithm. Character power for the specified interval can also be determined using Bloch spherical quantum genetic algorithm. Next, a roulette principal is employed, to determine one value from the character power data as a typical value of the mined power curve at the sample time. By connecting the typical power at each sample time, the typical daily power curve for BESS is achieved. Based on typical power curve, decision-maker can master the important operating parameters of BESS and analyze optimal capacity allocation. By error evaluation indexes between the mined typical daily power curve and power curve under different weather patterns, the simulation results verify that the mined power curve can address the operating power of the BESS under different weather patterns.

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Determining the load profiles of consumers based on fuzzy logic and probability neural networks

Cited by 76 publications

References 10 publications

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

CASSANDRA - A simulation-based, decision-support tool for energy market stakeholders

Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

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