An electricity price interval forecasting by using residual neural network

Chaweewat, Pornchai; Singh, Jai Govind

doi:10.1002/2050-7038.12506

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…The logistic sigmoid and hyperbolic tangent functions are the most important activation functions. 48,49 The unit activations from the first and preceding layer, each with x neurons for two hidden layers, can be expressed by Equations ( 4) and (5) as…”

Section: Influence Of Different Factors On Electrical Demand By Pcc Analysismentioning

confidence: 99%

“…This output is applied as an input to the next node until the convergence of the estimation process. The logistic sigmoid and hyperbolic tangent functions are the most important activation functions 48,49 . The unit activations from the first and preceding layer, each with x neurons for two hidden layers, can be expressed by Equations (4) and (5) as

{s^{1}}_{x} = f_{1} ((), \sum_{x = 1}^{h} {r^{1}}_{x} + \sum_{y = 1}^{n} \sum_{x = 1}^{h} {w^{1}}_{xy} i_{y})

{s^{p + 1}}_{x} = f_{p + 1} ((), \sum_{x = 1}^{h} {r^{p + 1}}_{x} + \sum_{y = 1}^{n} \sum_{x = 1}^{h} {w^{p + 1}}_{xy} {s^{p}}_{x})

…”

Section: Optimal Bra‐based Lf Strategy Incorporating Pccmentioning

confidence: 99%

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Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Waseem

Liu

Zhang

et al. 2021

Int Trans Electr Energ Syst

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With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies.To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to

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Section: Influence Of Different Factors On Electrical Demand By Pcc Analysismentioning

confidence: 99%

{s^{1}}_{x} = f_{1} ((), \sum_{x = 1}^{h} {r^{1}}_{x} + \sum_{y = 1}^{n} \sum_{x = 1}^{h} {w^{1}}_{xy} i_{y})

{s^{p + 1}}_{x} = f_{p + 1} ((), \sum_{x = 1}^{h} {r^{p + 1}}_{x} + \sum_{y = 1}^{n} \sum_{x = 1}^{h} {w^{p + 1}}_{xy} {s^{p}}_{x})

…”

Section: Optimal Bra‐based Lf Strategy Incorporating Pccmentioning

confidence: 99%

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Waseem

Liu

Zhang

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Several proposals can be found in the literature for global load forecasting, which gives the sum of all the loads of the entire system consisting of multiple substations. 7,8 Different statistical and intelligent techniques have been implemented for STLF of a single node, including ANN, [11][12][13][14] fuzzy logic, [15][16][17] genetic algorithm-based PSO, 16 classical regression techniques, 18,19 ARIMA method, 20,21 radial basis function neural networks, 22 support vector regression (SVR), 23 fuzzy art-map neural network, 24,25 wavelet-based decomposition methods, 26,27 etc. There are only a handful of papers dealing with multinodal load forecasting which predicts the loads at multiple nodes of a power system simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

NARX: Contribution ‐ factor‐based short‐term multinodal load forecasting for smart grid

Rai

2020

Int Trans Electr Energ Syst

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A novel multinodal load forecasting method is presented in this paper, which uses smart metered data available from a real-life distribution grid at the NIT Patna campus. Because of the dynamic nature of the load, individual loads need to be predicted simultaneously so that they represent the load of the same instant. The proposed method uses single-stage multinodal forecasting with and without the load contribution factor (LCF), thus reducing the complexity compared to existing two-stage multinodal forecasting methods while improving the forecasting accuracy. It utilizes a nonlinear autoregressive neural network model with exogenous input (NARX-NN), which uses its own predicted output as an input during forecasting; this improves the accuracy of the model and makes it less dependent on external input data compared to other variations of NN. The experimental results show that the proposed method outperforms the existing approaches for multinodal load forecasting of the practical distribution system under consideration. Under different input dataset scenarios, the average mean absolute percentage error (MAPE) of the proposed model is 1.44, which represents the best forecasting performance among the competing models. K E Y W O R D Sload contribution factor, multinodal load forecasting, NARX-NN model, short-term load forecasting, smart metering List of Symbols and Abbreviations: L n (h), load of n th node at hour h; TL(h), global load of the system at hour h; x, actual load of a node at a given instant; x min , minimum value of load of a node; x max , maximum value of load of a node; x new , normalized, load of a node at a given instant; y, variable to be predicted; x, exogenous variables used to predict y; n, m, number of time delays in input side, output side; φ, linear activation function for target variable; ;, non-linear activation function for hidden neurons; α 0 , output bias; α i , weights of the output layer; γ i0 , input bias; γ ij , input weights; i, index of n neurons; j, index of m inputs; δ ir , weight of feedback h r, t − 1 which is delayed by time t-1; R, Regression coefficient; y i , actual load; y͠ i , forecasted load; N, number of samples used for testing; AMI, Advanced metering infrastructure; ANN, Artificial neural network; ARIMA, Autoregressive integrated moving average; AH, All hour load; ACF

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Integrating Weather Conditions to Forecast the Electricity Price in Belgium Market: A Novel Mixed-Frequency Machine Learning Algorithm

Du,

Cai,

Tang

2024

J Syst Sci Complex

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An electricity price interval forecasting by using residual neural network

Cited by 7 publications

References 40 publications

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

NARX: Contribution ‐ factor‐based short‐term multinodal load forecasting for smart grid

Integrating Weather Conditions to Forecast the Electricity Price in Belgium Market: A Novel Mixed-Frequency Machine Learning Algorithm

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