Accurate electricity forecasting is crucial for grid stability and energy planning, especially in Benghazi, Libya, where frequent load shedding, generation deficits, and infrastructure limitations persist. This study proposes a data-driven approach to forecast electricity load, generation, and deficits for 2025 using historical data from 2019 (a year marked by instability) and 2023 (a more stable year). Multiple time series models were applied, including ARIMA, seasonal ARIMA, dynamic regression ARIMA, exponential smoothing, extreme gradient boosting, and Long Short-Term Memory (LSTM) neural networks. The dataset was enhanced through missing value imputation, outlier smoothing, and log transformation. Performance was assessed using mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error. LSTM outperformed all other models, showing strong capabilities in modeling non-stationary and seasonal patterns. A key contribution of this work is an optimized LSTM framework that integrates exogenous factors such as temperature and humidity, offering robust performance in forecasting multiple electricity indicators. These results provide practical insights for policymakers and grid operators to enable proactive load management and resource planning in data-scarce, volatile regions.

The use of cloud technology has become a mainstream practice in today's digital age. It has enabled businesses and individuals to store, share, and access data and applications from anywhere in the world. However, managing the cloud has become complex and challenging with the increasing volume of daily data. This is where artificial intelligence (AI) comes in. AI is revolutionizing how businesses manage their cloud infrastructure by enabling them to automate processes, identify trends, and make more informed decisions.

The concept of smart cities is gaining more and more traction in today's world, as cities around the globe are facing unprecedented challenges such as population growth, climate change, and an ever-increasing demand for resources. These challenges have made it necessary for city planners to think creatively and utilize innovative solutions to make cities more livable, sustainable, and efficient. One of the most promising technologies that are shaping the future of smart cities is artificial intelligence (AI). AI is revolutionizing urban planning by enabling cities to leverage big data to make smarter, data-driven decisions. By analyzing vast amounts of data generated by various sources such as sensors, cameras, and mobile devices, AI can help cities understand how people move, interact with their surroundings, and use resources.

Using a GPU (Graphics Processing Unit) can accelerate trading by allowing for faster processing of large amounts of data. This can be particularly useful for traditional banks, capital market firms and fintech companies that rely on data-intensive trading algorithms and need to process large amounts of data in real-time. Running machine learning algorithms: Machine learning algorithms can be computationally intensive, and a GPU can speed up the training process. This can be especially useful for developing and testing trading strategies that rely on machine learning. Data processing: A GPU can process large amounts of data quickly, which can be useful for tasks such as real-time data analysis and market monitoring.

Since the 2022, most of us in the entrepreneurial, start-up, creative, tech sphere have most certainly heard about the rise in AI tools. If you haven't then you are somewhere safe. The past year has shifted our possibilities to not only reach more consumers worldwide, but also we have witnessed the development of tools and software to produce some new limitless examples of the human imagination. I myself as a creative millennial became curious as to how I can increase my output with AI tools. Allow me share with you what I learned.

Continuing on with our blog series, today let's look at the construction estimating software market and how it is evolving. Today, there is an increasing need for cloud-based applications and mobile access, which is driving advancements in the construction estimating software market. In fact, Persistence Market Research suggests the global construction estimating software market is expected to grow from $340 million in 2021 to $357.4 million in 2022. Looking forward, it will grow to $556 million, showing a 4.5% growth rate from 2022-2032. Integration is one big estimating trend to watch in the construction industry.

Venture Capital (VC) is one of the most powerful mechanisms in modern society for catalyzing innovation – particularly in technology – however, ironically, the VC industry itself is not particularly innovative. The truth is that the VC's have been slow to adopt the AI technologies they help fund. VC is a business that historically, and even now, is deeply rooted in personal relationships, intuition, reputation and a very analog flow of ideas and information. However, the dominance of analog, purely intuitive approaches to early stage investing might be at an end. With the AI tools at our disposal, even now, we can eliminate major areas of uncertainty that currently – frankly – require guesswork, and which slow down decision making unnecessarily.

Business analytics give everyone from project managers to C-suite executives the ability to make better business decisions. Such tools turn otherwise unintelligible data – on customers, trends, products, and more – into valuable insights that facilitate astute decision-making and financial success. For all their popularity, traditional KPI dashboards and business intelligence (BI) tools have their limitations. Developing management reports or analytics dashboards is labor-intensive, and the subsequent information tends to be high-level summary data, pertaining to only a few key metrics. In fact, according to recent research, nearly half of enterprise decision-makers reported that the quality of data-based insights has decreased or plateaued over the past three years.

Artificial intelligence has demonstrated immense promise when applying machine learning to support the overall processing of large datasets, particularly in the banking and financial services industry. Sixty percent of financial services companies have implemented at least one form of AI, ranging from virtual assistants communicating with customers and the automation of workflows to managing fraud and network security. This largely stems from lack of understanding of how the system works and a continual concern around opacity, unfair discrimination, ethics and dangers to privacy and autonomy. These biases can create increased consumer friction, poor customer service, fewer sales and revenue, unfair or illegal behaviors, and potential discrimination. Achieving trustworthy AI requires close examination and the ability to identify what factors contribute to each bias to make a more informed decision about what actions should be taken after identification.

Climate change and global migration are at the centre of policymakers' efforts. The World Bank has dedicated an entire report to the topic of water, climate change, and development [1]. Together with the World Bank colleagues we have assembled a new dataset to make climate-induced migration visible with the use of explainable machine learning techniques. We showcase how social data science can help policy to make more informed decisions on some of the largest challenges of our times. Severe storms, rising sea levels, and prolonged droughts; the impact of climate change is real and it is increasingly impacting our life.