In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...

Computational intelligence approaches to multiple-step-ahead forecasting rely either on iterated one-step-ahead predictors or direct predictors. In both cases the predictions are obtained by means of multi-input single-output modeling techniques. This paper discusses the limits of single-output approaches when the predictor is expected to return a long series of future values and presents a mul...

We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and 10 min. We discuss optimal models for...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

Accurate PV power forecasting techniques are a prerequisite for the optimal management of grid and its stability. This paper presents review recent developments in field forecasting, mainly focusing on literature which uses ML techniques. The (sub-branch artificial intelligence) extensively used due to their ability solve nonlinear complex data structures. can either be direct, or indirect, inv...

In the recent past, COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type pandemic spread rapidly, posing a threat to human lives and economy. Because growing scale cases, employing artificial intelligence for future prediction purposes during this is crucial. Consequently, major objective research paper compare various deep learning forecasting alg...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey by optimally exploiting their properties. To do that, it compares the forecasting performance optimal pooling tilting techniques, including for predicting euro area inflation GDP growth at medium-term forecast horizons using both univariate multivariate met...

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

Roof-top mounted solar photovoltaic (PV) systems are becoming an increasingly popular means of incorporating clean energy into the consumption profile of residential users. Electric utilities often allow the inter-connection of such systems to the grid, compensating system owners for electricity production. As the systems grow in number and their contribution to the overall load profile becomes...

A model in which homebuyers make a modest approximation leads house prices to display three features present in the data but usually missing from perfectly rational models: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Valuing a house involves forecasting the current and future demand to live in the surrounding a...