Abstract
The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems.
Highlights
Energies 2021, 14, 7952. https://Electrical load forecasting plays a key role in the management and control of a power system
In the following steps, (9) the reference time series is additively disturbed with noise of different levels measured by the ratio of the standard deviations of the signal and noise (NSR—noise to signal ratio)
The designed research process consists of the following stages: (i) review of the scientific literature related to the methods in electrical load forecasting, which resulted in (ii) the identification of methods used in electrical load forecasting; (iii) review of the scientific literature related to the applications of auto-regressive integrated moving average (ARIMA) method in load forecasting, which resulted in (iv) the specification of ARIMA models employed in electrical load forecasting; (v) review of the scientific literature related to the noise impact on time-series forecasting
Summary
Electrical load forecasting plays a key role in the management and control of a power system. Breakdowns resulting from power system instability have serious implications for the sustainability of regional, national, and international energy systems. They may be a cause of many human systems failures and of serious environmental disasters. Precise analysis and forecasting of electric load are necessary to make rational decisions at all levels of energy sector control, management, and policy (technical, managerial, regulatory) as they are closely linked with countries’ energy security, resources, and natural environment. In the last dozen or so years, Clarivate WoS, the Scopus database, and IEEE Xplore. Xplore have severalorhundred eachthe year, in which key words include the have several hundredorpublications each year, in key words include termsrecorded
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