An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper

Observation of Enhanced Network Performance in IoT Process Control and Data Sensing with RINA

Internet of things (IoT) is one of the leading technologies which spanned from the trivial consumer applications to time-critical industrial applications. The current research in IoT focuses mostly on network performance as it is experiencing bottlenecks in data communication. IoT communication preferred UDP due to the limitations of TCP hard-state handshaking procedures on throughput. Proposed work developed a prototype with IoT devices communicating on a new internet architecture i.e. recursive inter-networking architecture (RINA) which has eliminated hard-state handshaking procedures. The impact of RINA on the network performance in process control and data acquisition is observed in terms of latency variations, network jitter and throughput. The results were compared against the network performance when the proposed prototype was communicating on TCP/IP. A Comparative analysis was provided to identify the improved network performance in RINA. This prototype was implemented in closed network configurations like LAN and WLAN in RINA as well as TCP/IP

Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach

Every energy system which we consider is an entity by itself, defined by parameters which are interrelated according to some physical laws. In recent year tremendous importance is given in research on site selection in an imprecise environment. In this context, decision making for the suitable location of power plant installation site is an issue of relevance. Environmental impact assessment is often used as a legislative requirement in site selection for decades. The purpose of this current work is to develop a model for decision makers to rank or classify various power plant projects according to multiple criteria attributes such as air quality, water quality, cost of energy delivery, ecological impact, natural hazard, and project duration. The case study in the paper relates to the application of multilayer perceptron trained by genetic algorithm for ranking various power plant locations in India

A survey of Multi-Criteria Decision Making Technique used in Renewable Energy Planning

Fossil based oil, gas and coal reserves will exhaust in few decades and the accelerated demand for conventional energy have forced planners and policy makers to look for alternate sources of Energy. Renewable energies option serves as a solutions for a sustainable, environmentally friendly and long-term cost effective sources of energies to meet our ever increasing needs of energy.  Renewable energy sites selection can be viewed as a Multiple Criteria Decision Making (MCDM) problem. MCDM is a complex Decision Making (DM) tools as it involves both quantitative and qualitative criteria. In recent years, several MCDM techniques and approaches have been suggested to solve energy planning problems. The main objective of this paper is to systematically review MCDM techniques and approaches in sustainable and renewable energy planning problems. A review of more than 100 published papers based on MCDM analysis is studied and presented in this paper. Findings of this review paper confirm that MCDM techniques can assist stakeholders and decision makers in unravelling some of the uncertainties inherent in renewable energy decision making. Classification of methodology used, criteria selection and application area are summarized and presented