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 from one day to one week ahead for the Spanish system operator

This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data and one day ahead hourly forecasts. Accordingly, forecast accuracy is assessed in terms of their errors. For doing so we analyze historical, real time forecasting errors for daily and hourly data for the year 2006, and report forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated

Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty

Forecasting from one day to one week ahead for the Spanish system operator

This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data and one day ahead hourly forecasts. Accordingly, forecast accuracy is assessed in terms of their errors. For doing so we analyze historical, real time forecasting errors for daily and hourly data for the year 2006, and report forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated.Energy forecasting, Hourly and daily models, Time series, Forecasting practice