Results 91 to 100 of about 45,826 (205)

Evaluasi Peramalan Permintaan Pupuk NPK Phonska di Jawa Tengah Menggunakan Metode Mean Absolute Scaled Error (MASE) [PDF]

Penyediaan pupuk subsidi sangat penting bagi produktivitas pertanian di Indonesia. Namun dalam penyediaanya terdapat risiko ketidaksesuaian antara suplai dan kebutuhan karena kebutuhan pupuk yang bersifat musiman.
Sari, Wangi Pandan, Dewayanto, Nugroho, Amdarso, Fajar Primazona   +2 more
core   +2 more sources

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

International Journal of Energy Research
In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry.
Khaoula Elhabyb, A. Baina, Mostafa Bellafkih, A. Deifalla   +3 more
semanticscholar   +1 more source

Least Squares Percentage Regression [PDF]

, 2007
The percentage error, or error relative to the observed value is usually felt to be more meaningful than the absolute error - yet is rarely used in the fitting of least squares regression models. We therefore explore least squares regression based on the
Tofallis, C.
core  

ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation [PDF]

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South ...
Mardewi, Mardewi, Nasruddin, Nasruddin, Aziz, Firman, Jeffry, Jeffry, Syam, Rahmat Fuady, Wungo, Supriyadi La   +5 more
core   +2 more sources

Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE

Scientific Reports
Particulate Matters PM $$_{2.5}$$ and PM $$_{10}$$ present a major health and environmental concern in urban regions. This research compares machine learning and time series models, such as Decision Tree (DT), Random Forest (RF), Support Vector ...
Waad Abuouelezz, Nazar Ali, Zeyar Aung, Ahmed Altunaiji, Shaik Basheeruddin Shah, Derek Gliddon   +5 more
doaj   +1 more source

Wind speed prediction in some major cities in Africa using Linear Regression and Random Forest algorithms

Journal of Nigerian Society of Physical Sciences
Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major
Timothy Kayode Samson, Francis Olatunbosun Aweda   +1 more
doaj   +1 more source

Calculating Value-at-Risk [PDF]

The market risk of a portfolio refers to the possibility of financial loss due to the joint movement of systematic economic variables such as interest and exchange rates.
William Fallon
core  

Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards

Buildings
As the distribution of indoor contaminants is often heterogeneous, the traditional Wells–Riley equation is inadequate for accurately assessing the infection risk to indoor personnel.
Chonggang Zhou, Yunfei Ding
doaj   +1 more source

Evaluating The Accuracy of Gridded Climate Datasets for Precipitation, Surface Air Temperature, and Sea Surface Temperature in Central Java, Indonesia

Indonesian Journal of Applied Physics
Studies of climate information that rely on accurate and reliable data are essential in hydrometeorological monitoring, early warning, and climate change impacts in areas with varied topography and limited observation data, such as Central Java ...
Iis Widya Harmoko, Muhammad Zainuri, Anindya Wirasatriya, Supari Supari   +3 more
doaj   +1 more source

COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS

Pilar Nusa Mandiri
A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution
Nendi Sunendar, Yan Rianto
doaj   +1 more source