Short-term load forecasting via ARMA model identification including non-gaussian process considerations

Huang, Shyh-Jier; Shih, Kuang-Rong

doi:10.1109/tpwrs.2003.811010

Cited by 547 publications

(55 citation statements)

References 27 publications

(30 reference statements)

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“…This section focuses the techniques for STLF. STLF is essentially a time series problem, and thus many traditional time series prediction techniques have been used to solve this problem, e.g., ARMA [23], ARIMA [24], and a hybrid of ARIMA and SVM [25]. Under normal conditions, these statistical techniques deliver good prediction results.…”

Section: Related Work On Short-term Load Forecastingmentioning

confidence: 99%

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Santra

Lin

2019

Energies

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Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long shortterm memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM's parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data.With the advance of computing power, deep neural network (DNN) has gained much popularity and been applied to STLF in recent years [36]. LSTM is a special type of DNN that is suitable for time series prediction due to its capability of remembering both the short-term and the long-term behavior in time series data. In Reference [37], two types of LSTM were compared against other deep learning techniques for predicting electricity load of every hour or every minute. Their results showed that LSTM outperformed the other techniques. However, all of the neural network approaches above require setting up the initial weightings of the links in the network, but poorly chosen weightings could lead the searching process trapped in the local optimum. A hybrid of LSTM and GA is proposed in Section 4 to resolve this problem.Prior to presenting our approach in Section 4, this section gives a detailed description of LSTM. LSTM is an augmented recurrent neural network model. It learns sequential information with long term dependencies, and preserves information for a long period of time. Traditional recurrent neural network suffers from the vanishing gradient problem. That is, as the number of layers using the same activation function increases, the gradients of the loss function approaches zero, making it difficult to train the network through backpropagation of errors. To prevent the vanishing gradient problem, LSTM utilizes memory cells, where each cell maintains a cell state and a hidden cell state, and uses three gates (namely, input gate, output gate, and forget gate) to control the flow of information into or out of the cell. A formal explanation of the LSTM model is given below.LSTM is for time series modeling, which maps an input sequence x = {x 1 , x 2 , . . . , x n } to an output sequence y = y 1 , y 2 , . . . , y n . For the STLF problem under study, each x i represents the hourly electricity load and the weather data of day i, and each y i represents the hourly electricity load of day i + 1, indicating a look-ahead parameter of value one. The LSTM contains layers of memory cells, where the interaction between the LSTM layers is shown in Figure 1, and the architecture of a LSTM cell is shown...

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Section: Related Work On Short-term Load Forecastingmentioning

confidence: 99%

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Santra

Lin

2019

Energies

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“…The most popular example of such statistical models is Box and Jenkins time series paradigm where load demands are estimated based upon a linear combination of their past values [13,14]. There are a large family of different models in this category that can deal with many special cases including seasonality, nonstationary, and non-homogeneity of variances (see e.g.…”

Section: Background Studymentioning

confidence: 99%

Modeling and forecasting of cooling and electricity load demand

et al. 2014

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h i g h l i g h t sWe propose a model for forecasting cooling and electricity load demand. The model takes the advantage of both time series and regression methods. The model is able to accurately forecast the load demands of the CCHP system. a r t i c l e i n f o t r a c tThe objective of this paper is to extend a statistical approach to effectively provide look-ahead forecasts for cooling and electricity demand load. Our proposed model is a generalized form of a Cochrane-Orcutt estimation technique that combines a multiple linear regression model and a seasonal autoregressive moving average model. The proposed model is adaptive so that it updates forecast values every time that new information on cooling and electricity load is received. Therefore, the model can simultaneously take advantage of two statistical methods, time series, and linear regression in an adaptive way. The effectiveness of the proposed forecast model is shown through a use case. The example utilizes the proposed approach for economic dispatching of a combined cooling, heating and power (CCHP) plant at the University of California, Irvine. The results reveal the effectiveness of the proposed forecast model.

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“…Over the last few decades, a wide range of techniques including statistical methods [5][6][7][8] and artificial intelligence (AI)-based methods have been proposed for electric load forecasting. Because an electric load series have striking characteristics such as nonlinearity and nonstationarity, statistical methods which assume stationary time-series have difficulty in predict-ing future electricity demand satisfactorily.…”

Section: Introductionmentioning

confidence: 99%

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Kim

2016

IJFIS

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Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

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Short-term load forecasting via ARMA model identification including non-gaussian process considerations

Cited by 547 publications

References 27 publications

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Modeling and forecasting of cooling and electricity load demand

Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

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