Long-term load forecasting via a hierarchical neural model with time integrators

Carpinteiro, O.A.S.; Leme, Rafael Coradi; Souza, Antônio Carlos Zambroni de; Pinheiro, Carlos A. M.; Moreira, Edmilson M.

doi:10.1016/j.epsr.2006.03.014

Cited by 59 publications

(23 citation statements)

References 12 publications

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“…Another MLP was proposed for result comparing purpose. The idea is to compare the results obtained in Carpinteiro et al (2007) with those of a rule-based model obtained with the use of the technique presented in Sect. 2.2.…”

Section: Experiments 1-electrical Charge Demand Seriesmentioning

confidence: 99%

“…The data for this experiment are described in Carpinteiro et al (2007), where two hierarchical hybrid neural models composed of two neural SOM networks-Self Organizing Map and a SLP-Single Layer Perceptron were used to forecast the monthly consumption of electrical energy 2 years ahead. The first model uses a gaussian function for the SOM networks, and the second one uses a discrete function.…”

Section: Experiments 1-electrical Charge Demand Seriesmentioning

confidence: 99%

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Time series forecasting through rule-based models obtained via rough sets

et al. 2011

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Prediction models based on artificial intelligence techniques have been widely used in Time Series Forecasting in several areas. They are often fuzzy models or neural networks. However, the use of rough sets based models have not yet been explored. The aim of this work is to introduce a new approach which uses rough set concepts to obtain rule-based models capable to perform time series forecasting.

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Section: Experiments 1-electrical Charge Demand Seriesmentioning

confidence: 99%

Section: Experiments 1-electrical Charge Demand Seriesmentioning

confidence: 99%

Time series forecasting through rule-based models obtained via rough sets

et al. 2011

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“…[28] Before that, Kermanshahi [91] in 1998 used ANN forecast load for 10 years, Ekonomou [92] used ANN to forecast load in Greece. Other commendable work in LTLF using ANN is reported in literatures [93][94][95][96][97][98][99][100].…”

Section: Long-term Load Forecasting Overviewmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

127

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…Hence, precise forecasting of electricity consumption is very important to balance supply and demand. On the basis of the difference of forecasting indicators, research on electricity consumption forecasting is usually divided into two categories: short-term [5,6,7,8] and mid/long-term [9,10,11]. As a representative research area of the latter, yearly electricity consumption forecasting plays an important role in electricity price adjustment and system expansion planning.…”

Section: Introductionmentioning

confidence: 99%

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

Xu¹,

Niu²,

Meng³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Yearly electricity consumption trends of most developing countries usually exhibit approximately exponential growth curves. An optimized nonhomogeneous exponential model (ONEM) is proposed as a method of forecasting electricity consumption by using trend extrapolation. The parameters of the nonhomogeneous exponential equation are obtained by using the inverse accumulated generating operation, discretizing the differential equation, minimizing the residual sum of squares (RSS), and accumulating the homogeneous exponential equation. Furthermore, to improve forecasting precision, particle swarm optimization (PSO) algorithm is used to optimize the equation parameters. To evaluate the forecasting performance for comparison, the said model and two other traditional methods are used to forecast the yearly electricity consumption of India. Empirical results show that this model is much better than traditional methods for each error analysis indicator.

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Long-term load forecasting via a hierarchical neural model with time integrators

Cited by 59 publications

References 12 publications

Time series forecasting through rule-based models obtained via rough sets

Time series forecasting through rule-based models obtained via rough sets

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

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