Short term load forecast based on time series analysis: A case study

Dodamani, Sateesh N.; Shetty, Vinay J.; Magadum, Rudresh B.

doi:10.1109/tapenergy.2015.7229635

Cited by 22 publications

(15 citation statements)

References 5 publications

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“…ARIMA and SARIMA utilize the lagged average values of STLF time-series data to convert non-stationary data into stationary ones. This seasonality can be observed by using autocorrelation (ACF) and partial auto-correlation (PACF) analysis [15]. Moreover, a review of several other statistical regression models and their variables and methods have been discussed in [16], such as single linear regression and multiple linear regression.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

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Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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“…Traditional neural networks generally only have two or three layers of neural networks, with limited parameters and calculation units, limited expression ability of complex functions and limited self-learning ability, while deep learning usually has five to ten layers or even more neural networks, and more effective algorithms are introduced. Literature [37], [38] proves that deep learning has a very accurate prediction accuracy in short-term load forecasting of natural gas.…”

Section: ⑥ Deep Learningmentioning

confidence: 99%

“…Traditional forecasting methods mainly include time series method, regression analysis method and grey theory forecasting method. Among them, time series method is regarded as the most classical, systematic, mature and widely used forecasting method [37]. Time series method was first used in power system load forecasting.…”

Section: A Initial Stagementioning

confidence: 99%

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

et al. 2020

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“…So far, many STLF models have been proposed based on various methods [5]. For instance, statistical models for STLF include autoregressive [6], regression analysis [7], and holt winters [8]. These models perform well when the input and output are linear, but degrade when the input and output are nonlinear [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM

Park¹,

Hwang²

2021

Sensors

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An efficient energy operation strategy for the smart grid requires accurate day-ahead electricity load forecasts with high time resolutions, such as 15 or 30 min. Most high-time resolution electricity load prediction techniques deal with a single output prediction, so their ability to cope with sudden load changes is limited. Multistep-ahead forecasting addresses this problem, but conventional multistep-ahead prediction models suffer from deterioration in prediction performance as the prediction range is expanded. In this paper, we propose a novel two-stage multistep-ahead forecasting model that combines a single-output forecasting model and a multistep-ahead forecasting model to solve the aforementioned problem. In the first stage, we perform a single-output prediction based on recent electricity load data using a light gradient boosting machine with time-series cross-validation, and feed it to the second stage. In the second stage, we construct a multistep-ahead forecasting model that applies an attention mechanism to sequence-to-sequence bidirectional long short-term memory (S2S ATT-BiLSTM). Compared to the single S2S ATT-BiLSTM model, our proposed model achieved improvements of 3.23% and 4.92% in mean absolute percentage error and normalized root mean square error, respectively.

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Short term load forecast based on time series analysis: A case study

Cited by 22 publications

References 5 publications

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM

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