The Peak Load Forecasting Afterwards Its Intensive Reduction

Maksimovich, Slobodan M.; Shiljkut, Vladimir M.

doi:10.1109/tpwrd.2009.2014267

Cited by 7 publications

(21 citation statements)

References 7 publications

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“…As for [7], it only needs one year of data in the learning phase, but achives a MAPE of 2.52%. The model proposed in this work, only requires two years of data, much lower values than at [5,6,8,9], and a similar values to the one used in [7], with the difference that the MAPE obtained with the model proposed here is 1.62%.…”

Section: Introductionmentioning

confidence: 96%

“…Another hybrid system is presented in [9], which includes three different solutions for annual PLF. In the worst case a MAPE of 32% and in the best case of 3.7% are obtained.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, [5] needs three years of data, [6] uses 10 years, [8] employs four years, [9] needs over 15 years to obtain certain indexes that will be used in the estimation and [10] employs five years. As for [7], it only needs one year of data in the learning phase, but achives a MAPE of 2.52%.…”

Section: Introductionmentioning

confidence: 99%

“…The ranges of consumption of the cited publications are: 500-3500 MW [4]; 1000-3000 MW [6]; 1000-1800 MW [7]; 2500-4600 MW [8]; 1350-1800 MW [9]; 20,000-42,000 MW [10]; 9000-15,000 MW [11]; and 200-400 MW [12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Hernández¹,

Baladrón

Aguiar

et al. 2013

Energies

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Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model. OPEN ACCESSEnergies 2013, 6 4490

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Section: Introductionmentioning

confidence: 96%

“…Another hybrid system is presented in [9], which includes three different solutions for annual PLF. In the worst case a MAPE of 32% and in the best case of 3.7% are obtained.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Hernández¹,

Baladrón

Aguiar

et al. 2013

Energies

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show abstract

“…The measures proposed and discussed pertain to the critical, winter season. Using and developing further the basic approach proposed in [16], we made a projection of the utility annual peak load, with simulated implementation of the DR/DSM program. For modeling peak load reduction, which would be achieved by DR/DSM measures, three alternative mathematical functions have been proposed and compared.…”

Section: Introductionmentioning

confidence: 99%

Long-term forecasting of annual peak load considering effects of demand-side programs

Rajaković

Shiljkut

2017

J. Mod. Power Syst. Clean Energy

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The main purpose of this research paper is to investigate the long-term effects of the proposed demandside program, and its impact on annual peak load forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.

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RETRACTED ARTICLE: Filter Bank Multicarrier Based Generalized Discrete Fourier Transform for Peak to Average Power Ratio Reduction

Ramesh¹,

Senthilkumar²,

Suresh³

et al. 2019

Mobile Netw Appl

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The Peak Load Forecasting Afterwards Its Intensive Reduction

Cited by 7 publications

References 7 publications

Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Long-term forecasting of annual peak load considering effects of demand-side programs

RETRACTED ARTICLE: Filter Bank Multicarrier Based Generalized Discrete Fourier Transform for Peak to Average Power Ratio Reduction

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