Accurate load forecasting is an important issue for the reliable and efficient operation of the power system. This paper presents a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural network), and DEKF (dual extended Kalamn filter) to construct a...

This paper proposes a hybrid STLF (short-term load forecasting) framework with a new input selection method. BNN (Bayesian neural network) is used to forecast the load. A combination of the correlation analysis and ℓ2-norm selects the appropriate inputs to the individual BNNs. The correlation...

The paper presents the application of echo state network (ESN) to short-term load forecasting (STLF) problem in power systems for both 1-h and 24-h ahead predictions while using the least number of inputs: current-hour load, predicted target-hour temperature, and only for 24-h ahead forecasting,...

In this paper, WESN (wavelet echo state network) with a novel ESN-based reconstruction stage is applied to both STLF (short-term load forecasting) and STTF (short-term temperature forecasting). Wavelet transform is used as the front stage for multi-resolution decomposition of load or temperature...

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption...

We forecast hourly solar irradiance time series using a novel hybrid model based on SOM (self-organizing maps), SVR (support vector regression) and PSO (particle swarm optimization). In order to solve the noise and stationarity problems in the statistical time series forecasting modelling...

Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR)...

This paper proposes an accurate short-term solar irradiance prediction scheme via support vector regression. Utilizing clearness index conversion and appropriate features, the support vector regression models are able to output satisfying prediction results. The prediction results are further...

Machine dynamics and soil elastic–plastic characteristic sort out the soil-wheel interaction productions as very complex problem to be estimated. Energy dissipation due to motion resistance, as the most prominent performance index of towed wheels, is associated with soil properties and tire...