Abstract:
The need for generation and management of electricity persists to grow every
year. The evolution in the total number of electric appliances (normally connected objects) per person is due primarily to the fast growth in the world’s
population and the increasing trend. In addition, residential sector is the
third largest consumer of energy in all economic sectors. Load Forecasting is
used mainly for predicting future loads of a particular system for a certain
time period. Short-term loads are usually thought to be a variable element
affected by various features such as historical load information, datasets of
weather elements such as precipitation, wind speed, temperature, air pressure and moisture. A precise forecasting with an individual model is almost
difficult. The main problem for utility companies in the world is to forecast
energy consumption.
Proposed approach presents a deployment of fuzzy time series for the
purpose of short-term load forecasting in a residential area. In this regard,
our work focuses on hourly, daily and weekly forecasting of electricity consumption for the historical data. The main contribution for our research is
to control the issue of overfitting and to enhance the accuracy of our system. There are several characteristics of fuzzy time series that make it more
desirable than traditional prediction systems. We shall show the improved
prediction system performance by simulations.
