Electricity Demand Forecasting Based on Threepoint Gaussian Quadrature and Its Application in Smart Grid

Wang, Xiao-Jia; Haijiang, Wang; Hou, Liqiang

doi:10.1109/wicom.2010.5600887

Cited by 4 publications

(2 citation statements)

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“…It shows the benefits of the smart use of AMI data in generation planning and load forecasting. Also, Wang et al introduce a data forecasting scheme for determining the electricity consumption a priori [6]. This scheme uses a three-point Gaussian quadrature approach to construct the forecasting model.…”

Section: Related Research Workmentioning

confidence: 99%

An early warning system against malicious activities for smart grid communications

et al. 2011

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here is high expectation recently for machine-tomachine (M2M) communications over wired and wireless links. Various applications of M2M have already started to emerge in various sectors such as healthcare, vehicular ad hoc networks, smart home technologies, and so on [1]. The evolution of M2M has also begun in developing a smart power grid framework, referred to as the smart grid (SG) [2,3]. An electric grid having smart or intelligent capability allows power providers, distributors, and consumers -operating requirements and capabilities. Through this awareness, smart grid is able to produce, distribute, and consume power in the most effective manner. This type of communication takes place only among machines such as sensors, smart meters, and other equipment. Therefore, the M2M communications in SG must be private and secure since many of the autonomic functions that will run over it will be critical. Smart grid is usually portrayed as having numerous electrical appliances connected to one another in a complex manner so that they can report back on information such as power consumption and other monitoring signals. This promises higher efficiency in the power distribution networks (i.e., greater availability of power to homes and factories at lower cost), and will allow distributed power generation such as local solar and wind generators. It will reach into home-based devices; therefore, in addition to scalability and fast communication, serious attention should be paid to SG security [4]. Since SG communication is going to be based on current networking technologies, the same security concerns often encountered in conventional networks will also be prevalent in SG. In fact, cyber threats such as distributed denial of service (DDoS) attacks are likely to have more impact on SG communication because of the involvement of so much electrical equipment on the consumer side. By means of early forecasting of malicious threats to SG, it may be possible to take quick measures to protect the appliances from being compromised by the attacker. To establish such a forecasting framework, however, we need to consider the fact that humans are not supposed to interfere with M2M communication. Instead, the machines within the SG should have an adequate framework to predict or warn about malicious events, abnormalities, and failures a priori. The machines in the SG can be found in different types of networks, such as home, building, and neighborhood-wide networks. The smart meters deployed in these different networks may be utilized to form an information sharing network. Such information sharing can provide an important resource for developing global and timely assessments of emerging malicious threats against SG communication.While smart meters should authenticate other smart meters and devices, the authentication scheme itself can be targeted by DDoS attackers. In this article, we consider the spread of a worm in an SG that compromises a number of machines (i.e., smart meters, electrical appliances), which start sending malic...

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Section: Related Research Workmentioning

confidence: 99%

An early warning system against malicious activities for smart grid communications

et al. 2011

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“…The authors used piecewise polynomial interpolation thought processing electricity consumption data to analyse the electricity consumption trends to make predictions. In references [6]- [7], Wang et al use Gauss orthogonalisation theory to improve the grey prediction model, and, in constructing the grey combinative interpolation model to forecast the electricity consumption of China, they achieved good prediction results. In addition, Wang also introduced Markov Chain theory to the grey combinative interpolation model, and constructed the Markov grey orthogonalisation model for electricity consumption prediction [8]- [9], which also obtained good prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Wang

Chen

Yang

2012

sajie

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Forecasting electricity consumption is one of the most important challenges in electricity system planning. This paper presents an improved semi-parametric regression model using the Student distribution function of residual to replace the nonparametric component of the traditional semi-parametric model, thus eliminating the effects of the residual disturbance term. Compared with general linear models, the models make statistical inferences and can automatically regulate the boundary effect, which gives the forecast result a higher accuracy. A case study using data from China is presented to demonstrate the effectiveness of the approach. OPSOMMINGDie vooruitskatting van elektrisiteitverbruik is een van die belangrikste uitdagings in elektrisiteitstelselbeplanning. Dié artikel bevat 'n verbeterde, semi-parametriese regressiemodel, wat gebruik maak van die Studentverdelingsfunksie van residuee om die nie-parametriese komponent van die tradisionele semi-parametriese model te vervang, en sodoende die effekte van die residuversteuringsterm uit te skakel. In vergelyking met algemene lineêre modelle, kan die model statistiese afleidings maak en outomaties die grenseffek reguleer, wat lei tot groter akuraatheid van die vooruitskatting. 'n Gevallestudie wat gebruik maak van data van China demonstreer die effektiwiteit van die benadering.

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Electricity Consumption Forecasting Based on a Class of New GM (1, 1) Model

Yao

Wang

2013

Lecture Notes in Electrical Engineering

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Electricity Demand Forecasting Based on Threepoint Gaussian Quadrature and Its Application in Smart Grid

Cited by 4 publications

References 0 publications

An early warning system against malicious activities for smart grid communications

An early warning system against malicious activities for smart grid communications

Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Electricity Consumption Forecasting Based on a Class of New GM (1, 1) Model

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