Study of food waste composting by using breadfruit peel as fermentation liquid

Food waste represents almost 60% of the total municipal solid waste disposed in the landfill. This is due to the lack knowledge and exposure of food waste recycling practice. Composting is one of low cost alternative method to dispose the food waste. The purpose of this research is to provide an alternative disposal method which is composting for food waste from MRMI in Parit Kuari Darat, Johor. The industry area is far from the main road and out of local authorities collection zone, and the solid waste management were perform improperly without collection and facilities provided. The aim of this study is to identify the physical, chemical and biological parameters of composting food waste from MRMI. The physical parameters are temperature, pH value and moisture content. Meanwhile, the chemical parameter are nitrogen, phosphorus, potassium, total organic carbon and heavy metals. As for biological parameters, bacteria count were tested during the study. Breadfruit peel was used as fermentation liquid because of it suitability and it is one of food waste that produced by MRMI and soil with coconut fiber were used as the decomposing medium. Takakura composting method was conducted in this study with 8 reactors which is reactors A1, B1, C1 and D1 (research compost) and reactors A2, B2, C2 and D2 (commercial compost). The results showed total food waste generated by MRMI is 1221.84 kg. In terms of chemical properties, the highest N content for research compost is 2240 ppm, P with 14.143 ppm and K with 704.5 ppm. Meanwhile, NPK content for commercial compost obtained the highest N value with 2268 ppm, P with 11.615 ppm and K with 645.55 ppm. In addition, TOC and C/N ratio for all reactors decreased significantly along the study and has reached the maturity stage. Traces of heavy metals were found lower than the standards. As the conclusion, research compost in this study is comparable with commercial compost and the NPK value for matured compost shows that the compost nutrient value is higher than organic fertilizer from previous study and the compost can be used as fertilizer and suitable for agricultural purposes

Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade

In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model

Prediction of power generation from a wind farm

Wind farms produce a variable power output depending on the wind speed. For management of power networks and for bidding for the supply of power, the future power available needs to be predicted for time intervals ahead of a few minutes to about 24 hours. This project used data from a wind farm and three meteorological stations to determine methods and ability to predict wind speed. Analyses using regression, neural networks, and a Kalman filter were examined. Prediction using a combination of local wind measure-ments and meteorological data appears to give the best results

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives

Elastic neural network method for load prediction in cloud computing grid

Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches

A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

Load Management in Power Grids - Towards a Decision Support System for Portfolio Operators

Decentralized renewable energy sources become more and more common. This leads to stability problems in power grids. Conventional energy sources are easy to control. In contrast, wind and solar power are much more difficult to forecast. Forecasts are only possible short term and are more imprecise. Producers and consumers of energy can try to help reducing stability problems. Contributions towards a decision support system are proposed and recommend how to alter the behavior of producers and consumers. On the producer side centrally controlled heat and power plants are able to shift load in a virtual power plant. The plant operator offers a load curve based on forecasts. The centrally controlled heat and power plants help to mitigate the effect of revised forecasts. An incentive based control on the consumer side is also proposed. Smart appliances react to pricing information. They alter their execution window towards the cheapest time slot, if possible. The exact behavior of appliances in the expected field experiment is still partially unknown. It is necessary to simulate the behavior of these appliances and to train an artificial neural network. The artificial neural network allows computing the pricing signal leading to a desired load shift