A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer s...

In this article, we summarize current forecasting practice, the performance of the forecasting enterprise, and the impacts of tropical cyclones from a meteorological perspective. In the past, a forecast was considered successful if it predicted the hurricane’s position and intensity 12–72 h into the future. By the 1990s, forecast users came to expect more specific details such as spatial distri...

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

Despite the great significance of precisely forecasting wind speed for development new and clean energy technology stable grid operators, stochasticity makes prediction a complex challenging task. For improving security economic performance power grids, accurate short-term is crucial. In this paper, deep learning model (Long Short-term Memory (LSTM)) has been proposed prediction. Knowing that t...

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in ord...

Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet tra...

The dayahead short term load forecasting (STLF) is a necessary daily task for power dispatch. Short term load forecasting is essential for unit commitment, economic allocation of generation, maintenance schedules. This paper presents a solution methodology using fuzzy logic for short term load forecasting. Fuzzy logic approach is implemented on weather sensitive data and historical load data fo...

Electrical load forecasting is well-known as one of the most important challenges in the management of electrical supply and demand and has been studied extensively. Electrical load forecasting is conducted at different time scales from short-term, medium-term and long-term load forecasting. Adaptive neuro-fuzzy inference system is a model that combines fuzzy logic and adaptive neuro system and...

 Abstract—This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology...

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...