Proposal for the Study of Short-Term Load Forecasting Multi-Regional Error Impact in the Operation Planning

Silva, L. N.; Abaide, Alzenira da Rosa; Negri, Valeria; Dornelles, A. G.

doi:10.1109/upec.2018.8542074

Cited by 1 publication

(1 citation statement)

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“…In such scenarios, the precision of predictive models becomes imperative. It plays a pivotal role in optimizing the dispatch of generators for cost-effective operations and maintenance (O&M) of the power system, a topic extensively discussed in [8,9].…”

Section: Introductionmentioning

confidence: 99%

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Figueiró,

Abaide,

Neto

et al. 2023

Energies

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Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%