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Traditional load forecasting methods,

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelligence techniques have been applied to short term load forecasting (STLF) with varying degrees of success. Although fuzzy regression has been tried for STLF for about a decade, most research work is still focused at the theoretical level, leaving little value for practical applications. A primary reason is that inadequate attention has been paid to the improvement of the underlying linear model. This application-oriented paper proposes a fuzzy interaction regression approach to STLF. Through comparisons to three models (two fuzzy regression models and one multiple linear regression model) without interaction effects, the proposed approach shows superior performance over its counterparts. This paper also offers critical comments to a notable but questionable paper in this field. Finally, tips for practicing forecasting using fuzzy regression are discussed.

Our goal is to characterize the traffic load in an IEEE802.11 infrastructure. This can be beneficial in many domains, including coverage planning, resource reservation, network monitoring for anomaly detection, and producing more accurate simulation models. The key issue that drives this study is traffic forecasting at each wireless access point (AP) in an hourly timescale. We conducted an extensive measurement study of wireless users on a major university campus using the IEEE802.11 wireless infrastructure. We propose several traffic models that take into account the periodicity and recent traffic history for each AP and present a time-series forecasting methodology. Finally, we build and evaluate these forecasting algorithms and discuss our findings.

Taylor, J.W. 2010. Triple Seasonal Methods for Short-term Load Forecasting. , 204, 139-152. ()

Neural networks for short-term load forecasting: a review and ..

ANN Based Short Term Load Forecastinf in Electricity Markets will be available on

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance.

Jeon, J., Taylor, J.W. 2016. Short-term Density Forecasting of Wave Energy Using ARMA-GARCH Models and Kernel Density Estimation. , 32, 991-1004.

The principal objective of short-term load forecasting (STLF) ..

As an essential energy in the daily life, electricity which is difficult to store has become a hot issue in power system. Short-term electric load forecasting (STLF) which is regarded as a vital tool helps electric power companies make good decisions. It can not only guarantee adequate energy supply but also avoid unnecessary wastes. Although there exists quantity of forecasting methods, most of them are not able to make accurate predictions. Therefore, a forecasting method with high accuracy is particularly important. In this paper, a combined model based on neural networks and least squares support vector machine (LSSVM) is proposed to improve the forecasting accuracy. At first, three forecasting methods named generalized regression neural network (GRNN), Elman, LSSVM are utilized to forecast respectively. Among them, simulate anneal (SA) arithmetic is used to optimize GRNN. Then, SA is employed to determine the weight coefficients of each individual method. At last, multiplying all the three forecasting results with the corresponding weights, the final result of the combined model can be attained. Using the electric load data of Queensland of Australia as experimental simulation, case studies show that the proposed combined model works well for STLF and the results prove more accurate.

Arora, S., Taylor, J.W. 2013. Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods. , 28, 3235-3242,

Canizares, Ajit Singh, "ANN- based Short- Term Load Forecasting in Electricity Markets", University of Waterloo, Canada
[2] Sanjib Mishra, Sarat Kumar Patra, "Short Term Load Forecasting using a Neural Network trained by A Hybrid Artificial Immune System", 2008 IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, Kharagpur, India December 8- 10, 2008
[3] J.P.S.

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Short-term load forecasting is very important for power system

Data mining for short-term load forecasting - IEEE

Ferreira, "Short Term electricity prices forecasting in a competitive market: A neural network approach", ScienceDirect, Electric Power Systems Research 77 (2007) 1297- 1304
[4] Ajay Gupta, Pradeepta K.

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