6 research outputs found
Short-Term Load Forecasting Using AMI Data
Accurate short-term load forecasting is essential for efficient operation of
the power sector. Predicting load at a fine granularity such as individual
households or buildings is challenging due to higher volatility and uncertainty
in the load. In aggregate loads such as at grids level, the inherent
stochasticity and fluctuations are averaged-out, the problem becomes
substantially easier. We propose an approach for short-term load forecasting at
individual consumers (households) level, called Forecasting using Matrix
Factorization (FMF). FMF does not use any consumers' demographic or activity
patterns information. Therefore, it can be applied to any locality with the
readily available smart meters and weather data. We perform extensive
experiments on three benchmark datasets and demonstrate that FMF significantly
outperforms the computationally expensive state-of-the-art methods for this
problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression
Tree and Support Vector Machine, respectively and up to 36% and 73.2%
improvement in MAPE over Random Forest and Long Short-Term Memory neural
network, respectively
A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR