Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …

Over the past few decades, many publications have been made in the area of Load
frequency control (LFC) of interconnected power systems. Load frequency control is …

Accurate load forecasting is helpful for optimizing the use of power resources. To this end,
this investigation proposes a hybrid model for short-term load forecasting, namely the RF …

We propose a combined method that is based on the fuzzy time series (FTS) and
convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in …

This paper proposes a novel framework for modeling electricity loads; it can be used for both
forecasting and analysis. The framework combines the EnKF (ensemble Kalman filter) …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

Nowadays electricity load forecasting is important to further minimize the cost of day-ahead
energy market. Load forecasting can help utility operators for the efficient management of a …

In recent years, the demand for developing low computational cost methods to deal with
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …

The establishment of fuzzy relations and the fuzzification of time series are the top priorities
of the model for predicting fuzzy time series. A lot of literature studied these two aspects to …

This paper proposes a new electric load forecasting model by hybridizing the fuzzy time
series (FTS) and global harmony search algorithm (GHSA) with least squares support vector …