Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Agricultural use of wastewater sludge from various sources with special emphasis on total and DTPA-extractable heavy metal content

This study was conducted to evaluate wastewater sludge from various sources for agricultural utilization. The results showed that sludge from municipal and food industrial wastewater treatment plants (WWTPs) have high fertilizing value with respect to nutrients and organic matter levels. When the sludge samples were evaluated for their total heavy metal contents, the Pb, Cd and Cu concentrations in all of the sludge samples were found to be below the limit specified by Turkish regulations. However, the Cr, Ni and Zn contents of domestic type, organized industrial zone, food industry sludge samples exceeded these thresholds. Other sludges were found to be suitable for agricultural usage in terms of plant nutrient and heavy metal content. The analysis of the sludge samples from twelve different WWTP’s showed that the agricultural properties and the total and bioavailable (DTPA-extractable) heavy metal fraction varies depending on the sludge samples. Therefore discussed sludges should be evaluated separately for the agricultural utilization potential in terms of soil pollution

Forecasting Turkey's short term hourly load with artificial neural networks

Load forecasting is important necessity to provide economic, reliable, high grade energy. In this study, short term hourly load forecasting systems were developed for nine load distribution regions of Turkey using artificial neural networks (ANN) approach. ANN is the most commonly preferred approach for load forecasting. The mean average percent error (MAPE) of total hourly load forecast for Turkey is found as 1.81%

Forecasting Turkey's Short Term Hourly Load with Artificial Neural Networks

Load forecasting is important necessity to provide economic, reliable, high grade energy. In this study, short term hourly load forecasting systems were developed for nine load distribution regions of Turkey using artificial neural networks (ANN) approach. ANN is the most commonly preferred approach for load forecasting. The mean average percent error (MAPE) of total hourly load forecast for Turkey is found as 1.81%