Accurate electricity load forecasting is of crucial importance for power system operation and smart grid energy management. Different factors, such as weather conditions, lagged values, and day types may affect electricity load consumption. We propose to use multiple kernel learning (MKL) for electricity load forecasting, as it provides more flexibilities than traditional kernel methods. Comput...

In this paper, the compactly supported orthonormal symmetrical wavelets is used to estimate non-uniformly sampled and non-gaussian noise corrupted load consumption signals for the purpose of load forecasting in a typical electrical utility network. Power load forecasting is an important function of utility management and present methods invariably rely heavily on past historical load curves whi...

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenc...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...

________________________________________________ AbstractPaper gives the compressed chronological work of scholars and comparison of classical and modern techniques to short term forecast the electrical load. This paper presents 10 approaches of load forecasting, their brief introduction and literature review. KeywordsLoad Forecasting, Classical Approach, Heuristic Approach, Fuzzy Logic, AI, AR...

This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system’s parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day...

Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and BoxJenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work present...

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is c...

This paper presents a two-stage service migration decision method which combines business workload forecasting with real-time load sensing, and thus adds business forecasting to previous load balancing approaches that rely solely upon real-time load sensing. The migration decision procedure and the detailed causal analysis algorithms based on Bayesian networks are also given. After the critical...

The main objective of this chapter is to present a hybrid model for bus load forecasting. This approach represents an essential tool for the operation of the electrical power system and the hybrid model combines a bus clustering process and a load forecasting model. As a case study, the model was applied to the real Brazilian electrical system, and the results revealed a performance similar to ...