Short-Term Load Forecasting in Smart Meters with Sliding Window-Based ARIMA Algorithms

Alberg, Dima; Last, Mark

doi:10.1007/978-3-319-54430-4_29

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…Matsila and Bokoro in [34] also presented a statistical time series method based on seasonal ARIMA (SARIMA) to predict electric load of a hospital in South Africa. Alberg and Last in [35] proposed two seasonal and two non-seasonal sliding window-based ARIMA methods for STLF in smart meters. These methods are integrated with the online information network(OLIN) methodology.…”

Section: Time Series Methodsmentioning

confidence: 99%

Electrical Load Forecasting: A methodological overview

Rostum

Zamel

Moustafa

et al. 2020

IJET

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Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data. Several researchers proved that, the presence of load forecasting error leads to an increase in operating costs. Thus Accurate electric load forecast is needed for power system security and reliability. It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.In recent times, there are significant proliferations in the implementation of forecasting techniques. This survey aids readers to summarize and compare the latest predominant researches on electric load forecasting. Besides, it presents the most relevant studies on load forecasting over the last decade and discusses the different methods that are used in load prediction as well as the future directions in this field.

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Section: Time Series Methodsmentioning

confidence: 99%

Electrical Load Forecasting: A methodological overview

Rostum

Zamel

Moustafa

et al. 2020

IJET

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“…This paper contributes to our conference paper Alberg and Last [1] in terms of a more detailed explanation of the proposed algorithms and presentation of new results. …”

Section: Related Workmentioning

confidence: 98%

Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Alberg

Last

2018

Vietnam J Comput Sci

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Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.

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“…In terms of statistical models, time‐series models have been used to capture the time‐series characteristics of power demand, e.g. ARMA [25, 26], ARIMA [27–29]. Beside time‐series models, Hong et al [30] adopt multiple linear regression to model the hourly energy demand using seasonality (regarding year, week and day) and temperature information.…”

Section: Related Workmentioning

confidence: 99%