scispace - formally typeset
Search or ask a question

What are the best practices for using PLN with a small database? 


Best insight from top research papers

The best practices for using PLN with a small database involve implementing the Jogja Losses Formula method to manage losses in the electricity sector . This method involves collecting data on assets, production, and sales, and using the Jogja Losses Formula to calculate the value of losses. The results are presented in a Chart Dupont, which divides losses into technical and non-technical categories for evaluation and analysis . Another best practice is to use a piecewise linear network, which is a neural network with three layers that is designed for fast function approximation with good generalization capabilities . This network can model strong nonlinear functions and the training is constructive, with the user providing an abort condition for when the training should stop . These best practices have been proven effective in managing losses in the electricity sector and approximating nonlinear functions with small databases.

Answers from top 3 papers

More filters
Papers (3)Insight
Proceedings ArticleDOI
W. Eppler, H.N. Beck 
10 Jul 1999
12 Citations
The paper does not provide specific best practices for using PLN with a small database.
The provided paper does not mention anything about using PLN with a small database. The paper is about the losses management of PT. PLN (Persero) ULP Siak using the Jogja Losses Formula calculation method.
The provided paper does not mention any specific best practices for using PLN with a small database.

Related Questions

What is pll?4 answersA Phase-Locked Loop (PLL) is a crucial component in electronic systems for generating stable output signals. It typically consists of a Voltage Controlled Oscillator (VCO), a loop filter, and a feedback control unit. PLL devices can operate as master devices with slave digitally controlled oscillators (sDCOs) in synchronization setups, facilitating the transfer of timing and frequency information between devices. In PLL frequency synthesizers, components like a phase comparison unit, charge pump, loop filter, and control unit work together to adjust the control voltage value for the VCO based on phase differences and control voltage change rates. PLLs can incorporate analog-to-digital converters, digital circuits, and digitally controlled oscillators to process signals and provide control signals for stable output signal generation. The design and optimization of PLLs are critical for maintaining stability and performance, especially in high-frequency applications.
Can LLMs be used for NLP?5 answersLarge Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, making them suitable for NLP applications. They have shown human-like cognitive abilities and can be used for knowledge-intensive tasks, natural language understanding tasks, natural language generation tasks, and more. LLMs can be enhanced by using external tools for question answering, which helps overcome challenges such as hallucination and weak numerical reasoning. Evaluating LLMs' abilities to use external tools is important, and new datasets like ToolQA have been introduced for this purpose. Additionally, an adaptive testing framework has been proposed for LLM evaluation, which dynamically adjusts the characteristics of test questions based on the model's performance, allowing for a more accurate estimation of the model's abilities. Practical guides are available to provide insights and best practices for working with LLMs in downstream NLP tasks.
Any studies using PLS regression to analyse data with small sample size?5 answersPartial least squares regression (PLS) has been used to analyze data with small sample sizes. PLS is a latent variable regression modeling approach that can capture the relationship between response and predictor variables in cases where there are a large number of predictors and small sample sizes compared to the number of predictors. PLS-SEM, a variant of PLS, has been specifically used in marketing research where small sample sizes are common. PLS has also been applied in the field of chemometrics, particularly in the analysis of spectral data sets, where it efficiently handles small sample sizes and multicollinearity problems. Additionally, PLS has been used in data mining for small-sample data analysis, demonstrating its potential for mining useful patterns and rules. The behavior of the PLS algorithm in the presence of missing data has also been studied, with various criteria proposed for selecting the number of components in PLS regression on incomplete data.
What is PLS in data-driven?4 answersPartial Least Squares (PLS) is a method used in data-driven research. It is a multivariate statistical technique that is commonly used in various fields such as process monitoring, neuroimaging ^[Context_2,, and economic benefit optimization. In the context of data-driven innovation (DDI), PLS is used to analyze the relationship between variables and drive effective DDI development. In neuroimaging research, PLS is employed to examine the interaction between brain activation and performance during working memory tasks ^[Context_2,. In the field of hydrometallurgy, PLS is used to establish models that optimize the economic benefit of gold cyanidation leaching process (GCLP). Overall, PLS is a versatile tool that allows researchers to analyze complex relationships between variables and make data-driven decisions in various domains.
What are the best practices for designing databases for large-scale applications?5 answersBest practices for designing databases for large-scale applications include carefully strategizing the literature search, structuring databases for multiple use, establishing version control, and making databases accessible. Researchers should carefully plan their literature search to ensure comprehensive coverage of relevant data. Databases should be structured in a way that allows for multiple uses, increasing efficiency and enabling broader research questions. Version control is crucial to maintain the integrity and accuracy of the database, both within the study and beyond. Finally, making databases accessible to the research community promotes transparency and collaboration, enhancing the overall quality and impact of the research. By following these best practices, researchers can design databases that are robust, efficient, and valuable for large-scale applications.
What are the best practices for knowledge management?3 answersSuccessful knowledge management practices require strong leadership support, employee buy-in, a culture of sharing information, a solid technological foundation, and an open and collaborative organizational culture. These practices lead to better employee engagement, productivity, innovation, customer satisfaction, and financial outcomes. To enhance knowledge management practices, organizations should foster a culture of knowledge sharing, ensure leadership backing, promote employee participation, and capitalize on technological infrastructure. Additionally, integrating and making knowledge accessible to all stakeholders, especially farmers, can enhance agriculture sectors and livelihoods. Small-scale farmers benefit from blending local and external knowledge systems, which improves communication, livelihoods, and economies within local communities. Policies should be adopted to promote the use of multiple knowledge systems by small-scale farmers and validate their innovations for wider dissemination. For soldiers, organizational leaders, and human resources professionals, best practices in knowledge management include implementing, developing, and maintaining knowledge management programs based on literature published after 2000.