Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models.

The global surge in energy demand, driven by technological advances and population growth, underscores the critical need for effective management of electricity supply and demand. In certain developing nations, a significant challenge arises because the energy demand of their population exceeds their capacity to generate, as is the case in Iraq. This study focuses on energy forecasting in Iraq, using a previously unstudied dataset from 2019 to 2021, sourced from the Iraqi Ministry of Electricity. The study employs a diverse set of advanced forecasting models, including Linear Regression, XGBoost, Random Forest, Long Short-Term Memory, Temporal Convolutional Networks, and Multi-Layer Perceptron, evaluating their performance across four distinct forecast horizons (24, 48, 72, and 168 hours ahead). Key findings reveal that Linear Regression is a consistent top performer in demand forecasting, while XGBoost excels in supply forecasting. Statistical analysis detects differences in models performances for both datasets, although no significant differences are found in pairwise comparisons for the supply dataset. This study emphasizes the importance of accurate energy forecasting for energy security, resource allocation, and policy-making in Iraq. It provides tools for decision-makers to address energy challenges, mitigate power shortages, and stimulate economic growth. It also encourages innovative forecasting methods, the use of external variables like weather and economic data, and region-specific models tailored to Iraq's energy landscape. The research contributes valuable insights into the dynamics of electricity supply and demand in Iraq and offers performance evaluations for better energy planning and management, ultimately promoting sustainable development and improving the quality of life for the Iraqi population.

Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

An Experimental Review on Deep Learning Architectures for Time Series Forecasting.

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.

Inferring gene regression networks with model trees.

BACKGROUND: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. RESULTS: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. CONCLUSIONS: REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.