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NILM applications: Literature review of learning approaches, recent developments and challenges
This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
An optimized model using LSTM network for demand forecasting
In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …
become increasingly important as it can help power companies in better load scheduling …
A machine learning approach to predict air quality in California
Predicting air quality is a complex task due to the dynamic nature, volatility, and high
variability in time and space of pollutants and particulates. At the same time, being able to …
variability in time and space of pollutants and particulates. At the same time, being able to …
Short-term load forecasting based on deep neural networks using LSTM layer
BS Kwon, RJ Park, KB Song - Journal of Electrical Engineering & …, 2020 - Springer
Short-term load forecasting (STLF) is essential for power system operation. STLF based on
deep neural network using LSTM layer is proposed. In order to apply the forecasting method …
deep neural network using LSTM layer is proposed. In order to apply the forecasting method …
Time-lag selection for time-series forecasting using neural network and heuristic algorithm
The time-series forecasting is a vital area that motivates continuous investigate areas of
intrigued for different applications. A critical step for the time-series forecasting is the right …
intrigued for different applications. A critical step for the time-series forecasting is the right …
A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting
The COVID-19 pandemic has disrupted the economy and businesses and impacted all
facets of people's lives. It is critical to forecast the number of infected cases to make accurate …
facets of people's lives. It is critical to forecast the number of infected cases to make accurate …
The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
In recent years, machine learning models based on big data have been introduced into
marketing in order to transform customer data into meaningful insights and to make strategic …
marketing in order to transform customer data into meaningful insights and to make strategic …
A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications
Accurate and real-time product demand forecasting is the need of the hour in the world of
supply chain management. Predicting future product demand from historical sales data is a …
supply chain management. Predicting future product demand from historical sales data is a …
Various optimized machine learning techniques to predict agricultural commodity prices
Recent increases in global food demand have made this research and, therefore, the
prediction of agricultural commodity prices, almost imperative. The aim of this paper is to …
prediction of agricultural commodity prices, almost imperative. The aim of this paper is to …