A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Short-term load forecasting plays a key role in energy optimizations such as peaking shaving and cost arbitrage. Forecasting the aggregated load of a city or region has been researched for years and produced accurate results with time lead ranging from an hour to a week. However, little attention has been paid to the building level due to the fact that its dynamics are considerably different from those of a utility or other middle or large-scale customers. This thesis work focuses on short-term load forecasting at a building level, which is more challenging and will be taken as the pre-work for peak shaving optimization by employing Battery Energy Storage System (BESS). The forecast method is based on Artificial Intelligence (AI) and Machine Learning which is the most flourishing field in the present time. Extreme Gradient Boosting (XG-Boost) and Correlation are used to filter redundant features, and Auto-correlation is used to find similar hours in the past. Support Vector Machines (SVM) with different kernel functions and Long short-term memory (LSTM) are applied for day-ahead and hour-ahead load forecasting. The data used in the modeling are hourly electricity load and meteorological data collected by University of Central Florida and the nearest climatological data station. The forecasting performances are estimated and show that LSTM works better in hour-ahead, day-ahead and peak hours prediction at the cost of longer training time. ; Korttidsprognoser för elförbrukningen spelar en nyckelroll vid energieffektivisering såsom att kapa lasttoppar och att sänka kostnaderna. Prognoser för den sammanlagda lasten i en stad eller en region har studerats under lång tid och det är möjligt att ta fram träffsäkra prongoser med en tidshorisont mellan en timme och en vecka. Däremot har prognoser på byggnadsnivå inte uppmärksammats, eftersom deras dynamik skiljer sig markant från dynamiken för en elförsäljare eller andra medelstora eller stora elkunder. Det här examensarbetet fokuserar på korttidsprognoser för lasten på ...
Short-term load forecasting plays a key role in energy optimizations such as peaking shaving and cost arbitrage. Forecasting the aggregated load of a city or region has been researched for years and produced accurate results with time lead ranging from an hour to a week. However, little attention has been paid to the building level due to the fact that its dynamics are considerably different from those of a utility or other middle or large-scale customers. This thesis work focuses on short-term load forecasting at a building level, which is more challenging and will be taken as the pre-work for peak shaving optimization by employing Battery Energy Storage System (BESS). The forecast method is based on Artificial Intelligence (AI) and Machine Learning which is the most flourishing field in the present time. Extreme Gradient Boosting (XG-Boost) and Correlation are used to filter redundant features, and Auto-correlation is used to find similar hours in the past. Support Vector Machines (SVM) with different kernel functions and Long short-term memory (LSTM) are applied for day-ahead and hour-ahead load forecasting. The data used in the modeling are hourly electricity load and meteorological data collected by University of Central Florida and the nearest climatological data station. The forecasting performances are estimated and show that LSTM works better in hour-ahead, day-ahead and peak hours prediction at the cost of longer training time. ; Korttidsprognoser för elförbrukningen spelar en nyckelroll vid energieffektivisering såsom att kapa lasttoppar och att sänka kostnaderna. Prognoser för den sammanlagda lasten i en stad eller en region har studerats under lång tid och det är möjligt att ta fram träffsäkra prongoser med en tidshorisont mellan en timme och en vecka. Däremot har prognoser på byggnadsnivå inte uppmärksammats, eftersom deras dynamik skiljer sig markant från dynamiken för en elförsäljare eller andra medelstora eller stora elkunder. Det här examensarbetet fokuserar på korttidsprognoser för lasten på ...
Radical Prosumer Innovations in the Electricity Sector and the Impact on Prosumer Regulation
| DOAJ | 2017
Short-term electricity load forecasting of buildings in microgrids
| Elsevier | 2015
Short-term Electricity Load Forecasting of Buildings in Microgrids
| Online Contents | 2015
Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning
| DOAJ | 2021