Modelling of electrical energy consumption in Delhi

Ranjan, Manish; Jain, V. K.

doi:10.1016/s0360-5442(98)00087-5

Cited by 89 publications

(28 citation statements)

References 7 publications

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“…4 and 6, the periodic characteristic of first-order difference operator is very obvious. In addition, decomposed signal 6 The IMFs and residue of the subseries one' first-order difference operator generated in noise eliminating process by EEMD for NSW Electricity Market started in 1st January 2011 and ended in 24th May, a total of 77040 data of 1605 days consists of the high frequency components in the short cycle, the middle frequency components in the middle cycle and the low frequency components in long period, because of that the periodic time series may be based on several years, quarter, month, week and day. Table 4 and Fig.…”

Section: The Simulation and Results Analysis Of The Two Seriesmentioning

confidence: 99%

“…Since the 1960s, it should come as no surprise to learn that an amassing number of researchers began to study load forecasting. For example, linear multiple regression models of electrical energy consumption in Delhi for different seasons have been developed in paper [6,7] presented a novel approach for short term load forecasting using fuzzy neural networks. The commonly used prediction methods include grey model, the traditional mathematical statistical model, artificial intelligence approach (spring up in the 1990s), combination model and hybrid model.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

Shen³

et al. 2017

Neural Process Lett

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Electricity load forecasting is an essential, however complicated work. Due to the influence of a large number of uncertain factors, it shows complicated nonlinear combination features. Therefore, it is difficult to improve the prediction accuracy and the tremendous breadth of applicability especially for using a single method. In order to improve the performance including accuracy and applicability of electricity load forecasting, in this paper, a concept named minimum cycle decomposition (MCD) that the raw data are grouped according to the minimum cycle was proposed for the first time. In addition, a hybrid prediction model (HMM) based on one-order difference, ensemble empirical model decomposition (EEMD), mind evolutionary computation (MEC) and wavelet neural network (WNN) was also proposed in this study. The HMM model consists of two parts. Part one, pre-processing, known as one order difference to remove the trend of subsequence and EEMD to reduce the noise, was performed by HMM model on each subset. Part two, the WNN optimized by MEC (WNN + MEC) was applied on resultant subseries. Finally, a number of different models were used as the comparative experiment to validate the effectiveness of the presented method, such as back propagation neural network (BP-1), BPNN combined MCD (BP-2), WNN combined MCD (WNNM), a HMM (DEEPLSSVM) based on one-order difference, EEMD, particle swarm optimization and least squares support vector machine and a hybrid model (DEESGRNN) based on one-order difference, EEMD, simulate anneal and generalized regression neural network. Certain evaluation measurements are taken into account to assess the performance. Experiments were carried out on QLD (Queensland) and NSW (New South Wales) electricity markets historical data, and the experimental results show that the MCD has the advantages of improving model accuracy and of generalization ability. In addition, the simulation results also suggested that the proposed hybrid model has better performance. B Zhaoshuang He

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Section: The Simulation and Results Analysis Of The Two Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

Shen³

et al. 2017

Neural Process Lett

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“…According to the factors already obtained, the demand drivers and the fundamental pillars in building a forecasting model [2][3][4] both need to be determined. Furthermore, methods of predicting electricity consumption precisely, effectively, and practically also need to be created.…”

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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“…Yan [3] proposed residential electricity consumption using climatic variables for Hong Kong. Rajan and Jain [4] expressed energy consumption patterns for Delhi as functions of weather and population. Fung and Tummala [5] concluded that it was reasonable to use electricity price, gross domestic product (GDP), deflated domestic exports and population to forecast electricity consumption in Hong Kong.…”

Section: Introductionmentioning

confidence: 99%

Forecasting electricity consumption in New Zealand using economic and demographic variables

Mohamed

Bodger

2005

Energy

235

103

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The influence of selected economic and demographic variables on the annual electricity consumption in New Zealand has been investigated. The study uses gross domestic product, average price of electricity and population of New Zealand during the period 1965-1999. Models are developed using multiple linear regression analysis. It was found that the electricity consumption correlated effectively with all variables. Forecasts made using these models were compared with some available national forecasts. The forecasts are also compared with the forecasts of the previously developed Logistic model. q

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Modelling of electrical energy consumption in Delhi

Cited by 89 publications

References 7 publications

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

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

Forecasting electricity consumption in New Zealand using economic and demographic variables

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