@article{article_266014, title={Short-term Load Forecasting based on ABC and ANN for Smart Grids}, journal={International Journal of Intelligent Systems and Applications in Engineering}, volume={4}, pages={38–43}, year={2016}, DOI={10.18201/ijisae.266014}, author={Cevik, Hasan Huseyin and Harmancı, Hüseyin and Çunkaş, Mehmet}, keywords={Artificial bee colony,Artificial neural network,Hybrid method,Short term load forecasting}, abstract={<p> <span style="font-size:12pt;font-family:’Times New Roman’, serif;">Short term load forecasting is a subject about estimating future
electricity consumption for a time interval from one hour to one week and it
has a vital importance for the operation of a power system and smart grids.
This process is mandatory for distribution companies and big electricity
consumers, especially in liberalized energy markets. Electricity generation
plans are made according to the amount of electricity consumption forecasts. If
the forecast is overestimated, it leads to the start-up of too many units supplying
an unnecessary level of reserve, therefore the production cost is increased. On
the contrary if the forecast is underestimated, it may result in a risky
operation and consequently power outages can occur at the power system. In this
study, a hybrid method based on the combination of Artificial Bee Colony (ABC)
and Artificial Neural Network (ANN) is developed for short term load
forecasting. ABC algorithm is used in ANN learning process and it optimizes the
neuron connections weights of ANN. Historical load, temperature difference and
season are selected as model inputs. While three years hourly data is selected
as training data, one year hourly data is selected as testing data. The results
show that the application of this hybrid system produce forecast values close
to the actual values. </span> <br /> </p>}, number={Special Issue-1}, publisher={İsmail SARITAŞ}