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

Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

On the impact of weather on German hourly power prices

The liberalization of electricity markets has triggered research in econometric modelling and forecasting of electricity spot prices. Moreover, both the demand and the supply of electricity are subject to weather conditions. Therefore, we examine the relation between hourly electricity spot prices from the European Energy Exchange and weather, represented by temperature and wind velocity. Furthermore, we assess whether the relation can be successfully exploited for forecasting. Thereby, we proceed in the framework of Markov regime-switching models which have become a workhorse in econometric modelling of electricity spot prices. As a result, we detect a strong relationship, on one hand. On the other hand, the significance of this relation for forecasting is confined to certain hours. --Electricity spot prices,Weather,Markov regime-switching

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014