ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise

Chodakowska, Ewa; Nazarko, Joanicjusz; Nazarko, Łukasz

doi:10.3390/en14237952

Cited by 60 publications

(27 citation statements)

References 76 publications

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“…The ARIMA Model is especially appealing since it uses logical and well-organized processes to create models utilizing the autocorrelation and partial autocorrelation functions [35]. During load forecasting, the relatively weak response of the ARIMA model to sudden disturbances makes it more preferable to implement [36]. In this research, a simple model MA and an advanced model ARIMA have been considered.…”

Section: Comparison With Other Modelsmentioning

confidence: 99%

Domestic Load Management With Coordinated Photovoltaics, Battery Storage and Electric Vehicle Operation

et al. 2023

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Coordinated power demand management at residential or domestic levels allows energy participants to efficiently manage load profiles, increase energy efficiency and reduce operational cost. In this paper, a hierarchical coordination framework to optimally manage domestic load using photovoltaic (PV) units, battery-energy-storage-systems (BESs) and electric vehicles (EVs) is presented. The bidirectional power flow of EV with vehicle to grid (V2G) operation manages real-time domestic load profile and takes appropriate coordinated action using its controller when necessary. The proposed system has been applied to a real power distribution network and tested with real load patterns and load dynamics. This also includes various test scenarios and prosumer's preferences e.g., with or without EVs, number of EV owners, number of households, and prosumer's daily activities. This is a combined hybrid system for hierarchical coordination that consists of PV units, BES systems and EVs. The system performance was analyzed with different commercial EV types with charging/ discharging constraints and the result shows that the domestic load demand on the distribution grid during the peak period has been reduced significantly. In the end, this proposed system's performance was compared with the prediction-based test techniques and the financial benefits were estimated.INDEX TERMS Autoregressive integrated moving average (ARIMA), coordinated operation, cost reduction, domestic load management, electric vehicle (EV), energy management, moving average (MA). NOMENCLATURE ψ EV max Maximum state of charge level. ψ EV min Minimum state of charge level.EV th EV's SOC threshold limit. EV t,χCharging or discharging state/mode of an EV.The associate editor coordinating the review of this manuscript and approving it for publication was Chandan Kumar . P PV + tot maximum power available from PV units. τ p Peak load. τ b Base load. ĈBES E Cost of the energy supplied by the battery storage system. µ DoD Depth of discharge.

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Section: Comparison With Other Modelsmentioning

confidence: 99%

Domestic Load Management With Coordinated Photovoltaics, Battery Storage and Electric Vehicle Operation

et al. 2023

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“…Common time series analysis methods include autoregressive moving average (ARMA), differential autoregressive moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), and so on. Reference [14] argued that there are different levels of noise disturbances in ARIMA forecasting short-term electricity loads, which require re-identification of the model before estimating parameters for the forecasting task, and that the model is able to determine the limit level of noise that the model can tolerate before crashing.…”

Section: Related Workmentioning

confidence: 99%

AWS-DAIE: Incremental Ensemble Short-Term Electricity Load Forecasting Based on Sample Domain Adaptation

Zhong

Lin

2022

Sustainability

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Short-term load forecasting is a prerequisite and basis for power system planning and operation and has received extensive attention from researchers. To address the problem of concept drift caused by changes in the distribution patterns of electricity load data, researchers have proposed regular or quantitative model update strategies to cope with the concept drift; however, this may involve a large number of invalid updates, which not only have limited improvement in model accuracy, but also insufficient model response timeliness to meet the requirements of power systems. Hence, this paper proposes a novel incremental ensemble model based on sample domain adaptation (AWS-DAIE) for adapting concept drift in a timely and accurate manner and solves the problem of inadequate training of the model due to the few concept drift samples. The main idea of AWS-DAIE is to detect concept drift on current electricity load data and train a new base predictor using Tradaboost based on cumulative weighted sampling and then dynamically adjust the weights of the ensemble model according to the performance of the model under current electricity load data. For the purposes of demonstrating the feasibility and effectiveness of the proposed AWS-DAIE algorithm, we present the experimental results of the AWS-DAIE algorithm on electricity load data from four individual households and compared with several other excellent algorithms. The experimental results demonstrated that the proposed AWS-DAIE not only can adapt to the changes of the data distribution faster, but also outperforms all compared models in terms of prediction accuracy and has good practicality.

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“…Time series methods rely on the historical load data and use statistical techniques to model the pattern of the load demand over time. Some famous examples of time series methods include ARIMA [6], Fuzzy series [7], and Multi-linear regression [8]. These methods assume that the future load demand will follow the same pattern as the past, with some random variation.…”

Section: Introductionmentioning

confidence: 99%

P-ConvLSTM: An Effective Parallel ConvLSTM-based model for Short-term Electricity Load Forecasting

Kshetrimayum,

Singh,

Hoque

2023

Preprint

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Short-term Load Forecasting (STLF) is a challenging task for an Energy Management System (EMS) that depends on highly unpredictable and volatile factors, making it difficult to predict the electricity load demand accurately. Despite the challenges, it is an essential component, as it helps to ensure energy demand-supply equilibrium, prevents blackouts, reduces the need for expensive peak power generation, and improves the efficiency and reliability of the EMS. Motivated by these factors, we have proposed a novel STLF framework using a multi-input parallel ConvLSTM model. The effectiveness of the proposed model is verified using two publicly available load-series datasets. On the Malaysia dataset, the proposed model yields 998.12, 2.59%, 1590.34, and 0.987 for MAE, MAPE, RMSE, and R2, respectively. Similarly, on the Tetouan dataset, this model yields 1737.32, 2.49%, 2254.91, and 0.976 for MAE, MAPE, RMSE, and R2, respectively. These outperforming results found in the comparative experiments are further statistically verified using Friedman's test. The presenting framework of STLF can help EMS to make informed decisions about resource allocation and system operations.

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ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise

Cited by 60 publications

References 76 publications

Domestic Load Management With Coordinated Photovoltaics, Battery Storage and Electric Vehicle Operation

Domestic Load Management With Coordinated Photovoltaics, Battery Storage and Electric Vehicle Operation

AWS-DAIE: Incremental Ensemble Short-Term Electricity Load Forecasting Based on Sample Domain Adaptation

P-ConvLSTM: An Effective Parallel ConvLSTM-based model for Short-term Electricity Load Forecasting

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