Electrical Load Forecasting based on Multiple Regression using Excel Tools

Bareth, Rashmi; Kochar, Matushree; Yadav, Anamika

doi:10.1109/icaect57570.2023.10117618

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…With the continuous development of the economy and society, the scale of the power system is expanding, and the components of the load are becoming increasingly complex, leading to a sharp increase in factors affecting power load. Existing short-term load forecasting software in power dispatch departments is finding it increasingly challenging to cope with the increasingly complex workload of load forecasting [3]. Often, manual intervention based on experience is required, further increasing the workload of operators and making it difficult to consistently meet the accuracy requirements of short-term load forecasting set by the government.…”

Section: Introductionmentioning

confidence: 99%

Online software design for short-term load forecasting based on design patterns

Li,

Chen

2024

J. Phys.: Conf. Ser.

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In response to the limitations inherent in methodologies reliant upon manual operations and practical experiences, commonly employed within the domain of load forecasting, such as convoluted operational procedures and suboptimal accuracy rates, this study introduces a novel online short-term load forecasting software solution. The developed software leverages cross-platform programming languages, notably Java and Python, alongside an object-relational mapping (ORM) framework to encapsulate database interactions, while adopting a browser/server (B/S) architecture. Furthermore, the software’s development process incorporates appropriate design patterns from object-oriented design (OOD), establishes an exception-handling architecture, and adheres to principles of test-driven development (TDD) to uphold superior software quality standards. Upon completion of construction and rigorous testing, the online short-term load forecasting software exhibits exceptional responsiveness to evolving functional requirements, thereby demonstrating its adaptability and scalability. Furthermore, its sustained operation over an extended duration underscores both its engineering feasibility and intrinsic software quality.

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Section: Introductionmentioning

confidence: 99%