One-hour-ahead load forecasting using neural network

Senjyu, Tomonobu; Takara, H.; Uezato, K.; Funabashi, Toshihisa

doi:10.1109/59.982201

Cited by 287 publications

(105 citation statements)

References 7 publications

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“…The flow chart has been shown in Figure 2 [11] . as the input to neural networks, neural network model being used to forecast for each layer of the wavelet coefficients in order to obtain the predicted value at ) ( T t  , finally these predicted values are used to reconstruct ) ( T t x  [12] .…”

Section: A Decomposition Forecast Reconstruction Forecasting Modelmentioning

confidence: 99%

A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS

Zhang¹,

Xia²

2017

IJITCS

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Abstract-A power system load forecasting method using wavelet neural network with a process of decomposition-forecasting-reconstruction and error analysis based on SPSS is presented in this paper. First of all, the load sequence is decomposed by wavelet transform into each scale wavelet coefficients of navigation. In this step, choosing an appropriate wavelet function decomposition of load is needed. In this paper, by comparing the signal-to-noise ratio (SNR) and the mean square error (MSE) of the different wavelet functions for load after processing; It is concluded that the most suitable wavelet function for the load sequence in this paper is db4 wavelet function. The scale of wavelet coefficients is obtained by load wavelet decomposition. In the process of wavelet coefficient of processing, the db4 wavelet function is used to decompose the original sequence in 3 scales; High frequency and low frequency wavelet coefficient is got through setting threshold. Secondly, these wavelet coefficients are used as the training sample of the input to the nonlinear regression neural network for processing, and then the forecasting result is obtained by the wavelet reconstruction. Finally, the actual and forecasting values are compared by SPSS with a comprehensive statistical charting capability, which is able to draw beautiful charts and is easy to edit.

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Section: A Decomposition Forecast Reconstruction Forecasting Modelmentioning

confidence: 99%

A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS

Zhang¹,

Xia²

2017

IJITCS

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“…The NN technique, incorporating a learning process, was applied for one-hourahead forecasting of power demand based upon a given temperature [17][18]. The volatility of market price was also measured by an NN application [19].…”

Section: B Previous Research On Power Tradingmentioning

confidence: 99%

Agent-Based Approach to Handle Business Complexity in U.S. Wholesale Power Trading

Sueyoshi

Tadiparthi²

2007

IEEE Trans. Power Syst.

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Abstract--This study documents the practicality of an agentbased approach by examining how two groups of agents handle business complexity related to power trading. Three important findings are identified in this research. First, the proposed approach can estimate fluctuations of electricity prices as well as other well-known methods such as neural networks and genetic algorithms. Second, multiple learning capabilities incorporated in adaptive agents do not have an advantage over limited learning capabilities in predicting the market price of electricity. Finally, a theoretical extension of multiple learning capabilities may have potential for developing the agent-based approach for power trading.

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“…The most important type of variable included in the input vector (IV) is the past timeseries of the variable being forecast (Hippert, 2001, Senjyu, 2002, Papalexopoulos, 1994and Khotanzad, 1994. Other variables, of an auxiliary nature, are used and, not being directly related to electricity consumption, they are usually represented by functions of the sinusoidal or binary type with the goal of helping the ANN to detect periodic features of the load behaviour (Drezga, 1998 andFidalgo, 1999).…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecast using trend information and process reconstruction

Santos

Martins

Pires

et al. 2006

Int. J. Energy Res.

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The algorithms for short-term load forecast (STLF), especially within the next-hour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalisation of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in next-hour load forecast. Designing an ANN requires, amongst other things, the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of process-reconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weather-related variables in the IV has also been analyzed, the option finally being to establish a model of the non-weather sensitive type. The paper uses a real-life case study.

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One-hour-ahead load forecasting using neural network

Cited by 287 publications

References 7 publications

A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS

A Loose Wavelet Nonlinear Regression Neural Network Load Forecasting Model and Error Analysis Based on SPSS

Agent-Based Approach to Handle Business Complexity in U.S. Wholesale Power Trading

Short-term load forecast using trend information and process reconstruction

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