Wind Energy Perspectives in Myanmar

One of Myanmar's problems is energy shortage. Partially, energy shortage can possibly be decreased by the construction of sizeable grid-connected offshore wind farms. Eight prospective construction sites were selected and wind turbine models chosen. This chapter describes the method for determining the optimal composition of the wind farms complex, consisting of several offshore wind farms located at a considerable distance from each other in areas with significant wind regime asynchrony. To illustrate this method, the optimal composition with an installed capacity of 47.6 MW and located off Myanmar's west coast is defined.

Development of DNN Model for Predicting Surge Pressure Gradient During Tripping Operations

Application of machine learning tools in drilling hydrocarbon well is still exploratory in its stage. This chapter presents a brief review of various applied research in drilling operations using machine learning (ML) tools and develop a deep neural network (DNN) model for predicting the downhole pressure surges while tripping. Tripping in or out drill-string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems, which can be avoided if pressure imbalances are predicted before this operation is engaged. Existing analytical models focus on forecasting the pressure imbalance but requires cumbersome numerical analysis. This could be solved by integrating DNN tool with the best existing analytical model predicted dataset. Consequently, the aim of this chapter is to provide an overview of various applications of machine learning tools in drilling and presenting a step-by-step process of developing a DNN model for the prediction of downhole pressure surges during tripping operation.

Selection of an Information Source and Methodology for Calculating Solar Resources of the Kyrgyz Republic

Detailed data on incoming solar radiation are needed in the design of solar energy systems of any scale: from large PV plants to small off-grid systems. However, in most cases, obtaining data on measurements of solar radiation is connected with difficulties due to financial or technical restrictions. Often, ground-based measurements of solar radiation are either not carried out at all or only the value of the global horizontal intensity of solar radiation is measured. The aim of the present study is to review and to verify some existing empirical models of the global solar radiation and its components for the climatic conditions of Kyrgyzstan as well as to estimate the applicability of Meteonorm database model for the available solar radiation in the territory of Kyrgyzstan. The necessity to select the most suitable models of the solar radiation is called by the lack of similar studies on this direction for the conditions of the country.

Improved Distributed Energy Systems Based on the End-User Consumption Profile

Nations are facing today the transition to cleaner, more reliable and affordable energy systems where power grids are becoming less centralized, more flexible and digitalized, and where not only power utilities, but also consumers are playing a significant role. Distributed Energy Systems (DES) constitute a key element in such transition, with decentralized renewable energy generation near the consumption points, energy storage, electric vehicles, and energy management systems, with the potential to ensure continuous supply and achieve higher efficiency, while reducing costs and adverse environmental impacts. This chapter presents a review of the recent advances in the design and development of DES, focusing on the effect of taking into consideration the consumption profile and behaviour of the end-users. The chapter also revises the limitations of DES and summarizes the future directions of DES development.

Application of Machine Learning Algorithms in Stock Market Prediction

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

Development of Box Behnken Design to Predict the Optimum Operating Condition of Rectangular Sheet Membrane to Increase Permeate Flux

The chapter focuses on the implementation of Box Behnken Design (BBD) to increase permeate flux of rectangular sheet membrane. Box Behnken Design (BBD) was used to optimize the membrane operation by predicting the optimum conditions. The factors such as operating pressure, feed velocity, and pore size were selected as the input of the model. The study illustrates the optimum conditions of operating pressure, feed velocity, and pore size, which was found to be 14.5Pa, 0.179 m/s, and 0.59µm respectively. Analysis of variance was used to identify the significant terms in the model equation. The effect of input parameters on the model output evaluated using Pareto analysis. It shows that operating pressure is the most significant parameter in the developed model. The BBD predicted results follow the actual results with high accuracy.

Wireless Technology of Electric Power Transmission Using Non-Metal Conductive Media

Over two million kilometers of power grids in Russia exist. They have to be replaced in the coming 15 years. However, nowadays we witness and participate in the development of advanced technologies. New wireless resonant electric-power systems for different power consumers are considered including stationary single-conductor transmission lines and single-trolley and noncontact high-frequency electric transport, using non-metal conductive media. The trends of the future development and application of wireless resonant systems for electric power transmission are described. In the future, electrified mobile robots with external wireless electric power supplies will allow the organization of agricultural production with full automation of technological processes. The chapter describes the method and means for electric power transmission without metal wires. In this case, several components of conventional transmission lines, such as metal wires, insulators, and cables, are excluded, which results in considerable reduction in the equipment cost.

Meta-Heuristic Approaches for the Optimization of Hydropower Energy

The concept of a multireservoir systems in hydropower introduces the function of multiple units simultaneously to reach the peak requirements. The reservoir optimized operation is a complex, extremely nonlinear, high dimensional, and multimodal task. The options that can be evaluated manually are generally limited in numbers, which made it difficult to identify the most appropriate option and should be taken into account while making decisions. Presently, for solving the optimization problems in multireservoir system, many modern heuristic stochastic search algorithms were established. That is possible because of the aspects of artificial and computational intelligence technologies. By connecting metaheuristic algorithms, the decision options can be identified to make the most suitable utilization of the scarce resources, best natural results for a given allotment can be attained, and the best trade-offs between contending goals can be established. In this chapter, the authors review the latest meta-heuristic optimization technique and their applications to maximize the economic factors.

IoT Limitations and Concerns Relative to 5G Architecture

Several current computer science applications, implemented within specific paradigms, work at different levels to solve various challenges facing particular sectors. The potential of the internet of things (IoT) in the context of fifth-generation networks (5G) is envisioned as suggesting several beneficial opportunities for companies, industries, and users to exploit this technology's applications. This chapter establishes how the IoT works, considering its 5G architecture. The emphasis is on the infrastructural characteristic in terms of transmission power, frequency, speed, security, localization, device lifetime, and others. Additionally, the chapter illustrates what the IoT entails, discussing its workability and efficiency. Furthermore, it highlights a range of newly distinguishing features that would give it much-touted success in comparison with other technologies. It also presents research issues and challenges.

Technique of Plant Electrical Stimulation by Weak Electric Currents

The impact of electricity is described on plant growth and vegetation. The application of the electrical currents is discussed from the point of view of plant cultivation improvement in horticulture. Underlining that the electricity is an abiotic stress stimulant, the electricity use ways classification is given. The application of the electric currents and other similar influences can—directly or indirectly—affect plants causing a series of physiological and biochemical reactions. This technique enables the yield optimization and the fruits quality improvement by regulating the intensity and duration of the exposure according to different types and kinds of vegetables. In the area of an effective technique development of the plant electric stimulation, there are many aspects almost impossible to be taken into account within one experiment. An option for solving such problems is the compilation of the experimental databases, the introduction of the smart control systems, and the management of the technological processes of plant electric stimulation.