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Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep…

The solar photovoltaic (PV) power generation industry has experienced substantial, ongoing growth over the past decades as a clean, cost-effective energy source. As electric grids use ever-larger proportions of solar PV, the technology’s inherent variability—primarily due to clouds—poses a challenge to maintaining grid stability. This is especially true for geographically dense, electrically isolated grids common…

Over the past decade, Bitcoin’s unprecedented performance has underscored its po-sition as the premier asset class. Starting from an insignificant value and reaching an astounding high of around 65,000 U.S dollars in 2021 – all without a central con-trolling authority – Bitcoin’s trajectory is undoubtedly a historical feat. Its intangible nature, initially a subject of…

This paper extends the idea of creating a Quantum Machine Learning classifier and applying it to real weather data from the weather station behind the Pa Sak Jonlasit Dam. A systematic study of classical features and optimizers with different iterations of parametrized circuits is presented. The study of the weather behind the dam is based…

Power losses exist naturally and have to be cared for in the operation of electrical power systems. Many researchers have worked on various methods and approaches to reduce losses by incorporating distributed generators (DG), particularly from renewable sources. These studies are based on the maximum unit penetration of the DGs, which is rarely achieved, resulting…

The increase of servers in data centers has become a significant problem in recent years that leads to a rise in energy consumption. The problem of high energy consumed by data centers is always related to the active hardware especially the servers that use virtualization to create a cloud workspace for the users. For this…

The ocean surface temperatures or sea surface temperatures have a significant influence on local and global weather. The change in sea surface temperatures will lead to the change in rainfall patterns. In this paper, the long-term rainfall forecasting is developed for planning and decision making in water resource management. The similarity of sea surface temperature…

Forecasting the gold price movement’s volatility has essential applications in areas such as risk management, options pricing, and asset allocation. The multivariate model is expected to generate more accurate forecasts than univariate models in time series data like gold prices. Multivariate analysis is based on observation and analysis of more than one statistical variable at…

Recently, numerous scholars have suggested fuzzy time series (FTS) models to forecast many different fields. One of the vital issues for high accurate forecasting in FTS model is method of partitioning in Universe of discourse (UoD). In this research, we propose a novel FTS model, which is established by using hedge algebra (HA) and particle…

The current concept of open innovation corresponds to the R&D products transfer model – “role changes”. One of the fundamental provisions of the model is that R&D products are considered for commercialization not only at the final stage of technological readiness, but at any of them. In today’s changing market environment, special attention is paid…

Enrolment figures have been expanding in South African institutions of higher-learning, however, the expansion has not been accompanied by a proportional increase in the percent- age of enrolled learners completing their degrees. In a recent undergraduate-cohort-studies report, the DHET highlight the low percentage of students completing their degrees in the allotted time, having remained between…

Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks,…

Short-term load forecasting (STLF) plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the…

The most important biological factors, mainly determining the economic efficiency of milk production, are the productivity potential and the level of viability of cows. The aim of the work is to predict the bio-economic effects in a heterogeneous population of dairy cows taking into account the decrease in the length of productive life with increase…

There is no doubt that a good knowledge of traffic demand has a direct impact on improving traffic management. Road traffic is strongly correlated with many factors such as day of week, time of day, season and holidays which make it suitable for prediction. In this paper, we develop a neural network model for hourly…

Climatic changes have a significant impact on many real life processes. Climacteric position of Albania makes precipitations and water inflows in HPP the main variables influencing the amount of electric energy produced in the country. Taking into account their volatility it has considerably increased the need of using hybrid models to improve the quality of…

Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use…

This manuscript offers an in-depth analysis of Explainable Artificial Intelligence (XAI), em- phasizing its crucial role in developing transparent and ethically compliant AI systems. It traces AI’s evolution from basic algorithms to complex systems capable of autonomous de- cisions with self-explanation. The paper distinguishes between explainability—making AI decision processes understandable to humans—and interpretability, which provides…

Aquaculture is one of the most important industries worldwide, and most marine products are produced by aquaculture. On the other hand, the aquaculture farmers are faced on the challenge of damage to marine products due to abnormal seawater temperatures. Research on seawater temperature prediction have been conducted, but many of them require a large amount…

While Virtual reality is becoming more popular, 360-degree video transmission over the Internet is challenging due to the video bandwidth. Viewport Adaptive Streaming (VAS) was proposed to reduce the network capacity demand of 360-degree video by transmitting lower quality video for the parts of the video that are not in the current viewport. Understanding how…

The integration of fluctuating renewable resources such as wind and solar into existing power systems poses challenges to grid reliability and the seamless incorporation of these resources. To address the inherent variability in renewable generation, direct load control emerges as a promising method for demand-side management. Thermostatically controlled appliances, like air conditioners, hold a significant…

Cybercriminal exploits integrity, confidentiality, and availability of information resources. Cyberattacks are typically invisible to the naked eye, even though they target a wide range of our digital assets, such as internet-connected smart devices, computers, and networking devices. Implementing network anomaly detection proves to be an effective method for identifying these malicious activities. The traditional anomaly…

The Ministry of Finance is the state ministry in charge of state financial affairs which has two functions, namely the Chief Financial Officer (CFO) as the State General Treasurer and the Chief Operating Officer (COO) as a Budget User. As COO, the Ministry of Finance is expected to be able to provide information related to…

Forecasting food prices play an important role in livestock and agriculture to maximize profits and minimizing risks. An accurate food price prediction model can help the government which leads to optimization of resource allocation. This paper uses ridge regression as an approach for forecasting with many predictors that are related to the target variable. Ridge…

This paper examines the impacts of the COVID-19 pandemic and selected commodity variables on Booking.com share price using the Markov-switching approach. Daily data spans from January 2017 through July 2020 are utilized in this study. Empirical evidence showed that COVID-19, international crude oil price, and gold price affected the Booking.com share price significantly. A positive…