Very short-term electricity load demand forecasting using support vector regression.
ABSTRACT In this paper, we present a new approach for very short term electricity load demand forecasting. In particular, we apply support vector regression to predict the load demand every 5 minutes based on historical data from the Australian electricity operator NEMMCO for 2006-2008. The results show that support vector regression is a very promising approach, outperforming backpropagation neural networks, which is the most popular prediction model used by both industry forecasters and researchers. However, it is interesting to note that support vector regression gives similar results to the simpler linear regression and least means squares models. We also discuss the performance of four different feature sets with these prediction models and the application of a correlation-based sub-set feature selection method.
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ABSTRACT: This paper presents a new methodology for electricity demand forecasting on very short-term horizon based on a discrete probabilistic model (Markov Chain). The modeling process is automated by a feature extraction tool, the Self-Organizing Map, considering historical data of climate variables (air temperature, relative humidity and wind speed) and load behavior, related through the thermal discomfort index and wind chill. Thus, it is possible to estimate the probability of a certain demand level occur given a current climatic condition, as well as the number of time intervals (hours) until this occurs. The forecast is then used to control the decentralized dispatch of a small hydroelectric power plant, aiming to minimize overtaking the transmission contract.Electric Power Systems Research 07/2014; 112:27–36. DOI:10.1016/j.epsr.2014.03.005 · 1.60 Impact Factor
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ABSTRACT: This paper presents a quarter-hourly ahead load forecasting method using the Artificial Neural Network (ANN). The proposed method was designed and programmed using the National Instrument (NI) LabVIEW software tool. The architecture of the ANN is a three-layer feed forward neural network and its estimation technique is based on backpropagation (BP). The performance of the network is enhanced by implementing an Early Stopping (ES) algorithm to avoid the overfitting of the training data. In order to test the performance of the algorithm, historical load data obtained from Energy Market Company (EMC) in Singapore were used in training the ANN and satisfactory results were obtained. The proposed ANN-based load forecast technique was tested with two different types of training data namely the actual load and the relative incremental of the actual load. The results are discussed in the paper. The algorithm is integrated into the Microgrid Energy Management System (MG-EMS) at Laboratory for Clean Energy Research (LaCER), School of Electrical & Electronic Engineering in Nanyang Technological University (NTU). The daily forecast data is useful to Unit Commitment (UC) for minimizing schedule cost or maximizing revenue of MG-EMS.PowerTech, 2011 IEEE Trondheim; 01/2011
Conference Paper: Short-term electricity demand forecasting method for smart meters[Show abstract] [Hide abstract]
ABSTRACT: The short-term electricity demand forecasting has become one of the major research area in power system engineering. By combining the smart metering to the short-term demand forecasting techniques, new features can be added to save on demand and electricity bill. This paper illustrates the methodology used to forecast electricity demand over short period of time which can be used with smart meters. Polynomial fitting with interpolation is used to forecast the demand by taking the apparent power sample points from smart meters. The outcome of this work will be beneficial to the residential or industrial electricity consumers to control the demand side loads. It will help the industrial consumers to save on maximum demand charge with the introduction of warning message or residential consumers to reduce their electricity bill by cutting down non-essential loads in peak hours.Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on; 01/2012