Classification of electricity load forecasting based on the factors influencing the load consumption and methods used: An-overview

Mustapha, Mamunu; Mustafa, Mohd Wazir; Khalid, Saifulnizam Abd.; Abubakar, I.; Shareef, Hussein

doi:10.1109/cencon.2015.7409585

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…A long-term electricity projection model, based on the relationship between the electricity load and relevant driving parameters, has been developed in this paper. In the model the most commonly discussed driving parameters, such as existing load data, economic data, annual electricity consumption, annual peak load, temperature and some country-specific economic data are applied [33]. In addition to the common parameters mentioned in the literature, new parameters such as the time of sunset, tourism, travel share in GDP, and proportion of the electricity generation share of hydro, nuclear and geothermal power plants are introduced.…”

Section: Discussionmentioning

confidence: 99%

“…Determination of the relevant variables affecting electricity demand and selecting the appropriate model basis are important steps in modelling and projecting electricity demand. Several applied parameters are well known and most commonly used in the literature [33], such as existing load data, economic data, annual electricity consumption, annual peak load, temperature and some country-specific economic data.…”

Section: Datamentioning

confidence: 99%

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Long term load projection in high resolution for all countries globally

Toktarova

Gruber

Hlusiak

et al. 2019

International Journal of Electrical Power & Energy Systems

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Electricity demand modelling is the central and integral issue for the planning and operation of power systems. Load projection provides important information for electricity network planning, and it is essential for the electricity system development. This work investigates the impact of specific economic, technical and climate characteristics on the shape of the electricity demand and introduces a methodology to project electricity demand in hourly resolution within a single framework for all countries. The method used is a multiple linear regression in terms of spectral analysis. 57 real load data profiles of diverse countries were decomposed into a set of sine functions to analyse the cyclical pattern of the data. Fourier coefficients contain information about frequencies and amplitudes in these sinusoids. The sum of various sine functions can be used to calibrate and project hourly electricity demand for any country with available input data for any year in the addressed period. The accuracy of proposed model function is represented in terms of R-squared error. The proposed model is flexible to be applied to different socio-economic scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections.

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Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Long term load projection in high resolution for all countries globally

Toktarova

Gruber

Hlusiak

et al. 2019

International Journal of Electrical Power & Energy Systems

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“…After training ANN and executing the hybrid algorithm, values of peak load and valley load forecasting on 19 November 2016 were, respectively, 7769.8 MW and 6134.1 MW. Using Equations (1) and (2), the results are shown in Figure 10. After training ANN and executing the hybrid algorithm, values of peak load and valley load forecasting on 19 November 2016 were, respectively, 7769.8 MW and 6134.1 MW.…”

Section: Forecasting For 19 Novembermentioning

confidence: 99%

“…After training ANN and executing the hybrid algorithm, values of peak load and valley load forecasting on 19 November 2016 were, respectively, 7769.8 MW and 6134.1 MW. Using Equations (1) and (2), the results are shown in Figure 10. As shown in Figure 11, the largest error was 3.736% at 12:00, the smallest was 0.018% at 04:00 and the average error was 1.153%.…”

Section: Forecasting For 19 Novembermentioning

confidence: 99%

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Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Pham

Nguyen

et al. 2019

Energies

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Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

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Research on Load Forecasting Method Considering Data Feature Analysis Based on Bi-LSTM Network

Ding

Jia

et al. 2022

Communications in Computer and Information Science

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Classification of electricity load forecasting based on the factors influencing the load consumption and methods used: An-overview

Cited by 19 publications

References 34 publications

Long term load projection in high resolution for all countries globally

Long term load projection in high resolution for all countries globally

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Research on Load Forecasting Method Considering Data Feature Analysis Based on Bi-LSTM Network

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