@article{(Open Science Index):https://publications.waset.org/pdf/9999697,
	  title     = {Evaluation of Short-Term Load Forecasting Techniques Applied for Smart Micro Grids},
	  author    = {Xiaolei Hu and  Enrico Ferrera and  Riccardo Tomasi and  Claudio Pastrone},
	  country	= {},
	  institution	= {},
	  abstract     = {Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {8},
	  number    = {11},
	  year      = {2014},
	  pages     = {1995 - 2000},
	  ee        = {https://publications.waset.org/pdf/9999697},
	  url   	= {https://publications.waset.org/vol/95},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 95, 2014},