Long-Term Load Forecasting Using System Type Neural Network Architecture

Accurate load forecasting is essential for power system planning and operation. As the electricity end uses expand, load forecasting technique becomes more complicated. Depending on the nature of load variations, one particular method may not be suitable in each case. Before choosing a particular method, an overview of different techniques is required. This paper presents the analysis of various techniques for load forecasting. A comparative analysis is presented to understand the performance of each technique. None of them is found to be perfect alone; the trend is towards hybrid models.Keywords-load forecasting, short term load forecast, long term load forecast.I.

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“…Literature shows that ANN has been applied mainly for short term. But it is also used for long-term load forecasting [15,16,17,18].…”

Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

Analysis and comparison of various methods available for load forecasting: An overview

Khatoon

Ibraheem

Singh

et al. 2014

2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls With Their Impact on Humanity (CIPECH)

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“…In previous papers [3][4][5][6][7][8] , a system-type neural network which implemented extrapolation was proposed. In this method, the distributed parameter system (DPS) surface determined by a given data set was expanded along one axis.…”

Section: Proposed Methods a System-type Neural Network Methodsmentioning

confidence: 99%

Short-Term Load Forecasting Using System-Type Neural Network Architecture

Kim¹,

Velas²,

Lee³

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Neural networks have been applied in various new ways to the problem of short-term load forecasting for power systems. Virtually all of these methods are based on using statistical patterns, which are perceived between the yearly load histories of the system to predict the forecasted year's demand. The proposed method also uses a neural network approach, but differs from the others in how those patterns are perceived. Specifically, the proposed approach begins with the premise that the load demand for a given year can be given a structure which can then be related to the structure of the reference year, in such a way that a transformation can be found from the reference year's structure to the forecasting year's structure. The transformation depends upon how parameters, which influenced the load but can not be measured, move from the reference year to the forecasting year.

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“…The outputs of the orthonormal vectors are (internally) linearly summed so that the channel spans an n-dimensional function space. The Semigroup Channel can be adapted from the Diagonal Recurrent Neural Network (DRNN) or the Elman architecture 8 , in which the input is split into a dynamic scalar component T and one static vector component, the initial vector ) 0 ( C . The output is a vector…”

Section: Proposed Methods a System-type Neural Network Methodsmentioning

confidence: 99%

Short-Term Load Forecasting Using System-Type Neural Network Architecture

Du¹

2009 GSW Proceedings

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This paper presents a methodology for short-term load forecasting using a system-type neural network based on semigroup theory. A technique referred to as algebraic decomposition is proposed for the modeling of electric power load demand in terms of the coefficient vector and the basis vector, and a new learning algorithm based on semigroup theory is put forward for extrapolation of the coefficient vector. Due to the non-stationary attribute of the load, the actual load is preprocessed by regression to become better correlated to daily time and temperatures. A rearrangement method based on the hourly temperature is developed to solve the problem of the roughness of the coefficient vector. With the algebraic decomposition of the rearranged regression load, a much smoother coefficient curve can be obtained. Based on the smoothness, interpolation and extrapolation can be achieved for each hour using the historical hourly temperatures and the hourly temperature forecast. The interpolated or extrapolated coefficient vector is recombined with the basis vector for each hour, and the recombined hourly load are grouped to form the final load forecast of the target day. A moving window slides through the whole year to perform the day-ahead load forecasting. Load data from New England Independent System Operator (ISO) is used to verify the capability of the proposed approach.

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Long-Term Load Forecasting Using System Type Neural Network Architecture

Cited by 8 publications

References 14 publications

Analysis and comparison of various methods available for load forecasting: An overview

Analysis and comparison of various methods available for load forecasting: An overview

Short-Term Load Forecasting Using System-Type Neural Network Architecture

Short-Term Load Forecasting Using System-Type Neural Network Architecture

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