Conference Paper

Very short-term electricity load demand forecasting using support vector regression

DOI: 10.1109/IJCNN.2009.5179063 Conference: International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, Georgia, USA, 14-19 June 2009
Source: DBLP

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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    • "Setiawan et al (Setiawan, A. et al, 2009) have presented a new approach for the very short-term electricity load demand forecasting using SVR. Support vector regression was applied to predict the load demand every 5 minutes based on historical data from the Australian electricity operator NEMMCO for 2006-2008. "
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    ABSTRACT: Electric load forecasting is a real-life problem in industry. Electricity supplier’s use forecasting models to predict the load demand of their customers to increase/decrease the power generated and to minimize the operating costs of producing electricity. This paper presents the development and the implementation of three new electricity demand-forecasting models using the adaptive neuro-fuzzy inference system (ANFIS) approach in parallel load series. The input-output data pairs used are the real-time quart-hourly metropolitan France electricity load obtained from the RTE website and forecasts are done for lead-time of a 1 h ahead. Results and forecasting performance obtained reveal the effectiveness of the third proposed approach and shows that 56 % of the forecasted loads have an APE (absolute percentage error) under 0.5, and an APE under one was achieved for about 80 % of cases. Which mean that it is possible to build a high accuracy model with less historical data using a combination of neural network and fuzzy logic.
    Full-text · Chapter · Dec 2014
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    • "It can be seen in the literature that the projections of electricity demand are basic for power system studies such as expansion, planning and operation. However, it can be held on different time horizons like long-term (1–10 years), medium-term (several months to a year), short-term (a day ahead) and very short-term (hours and minutes ahead), depending on the planning objectives [1]. The estimation of future behavior of electric loads is directly linked to the task of decision making and achievement of management actions on both the demand and supply side. "
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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.
    Full-text · Article · Jul 2014 · Electric Power Systems Research
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    • "Difference of the previous forecast day and its similar days' load, temperature and humidity were input to the fuzzy system involving three input membership function. Different feature sets of the historical load were selected in [3] for daily load forecasting. Beside historical data, the electricity price forecasting is also affected by its load demand. "

    Full-text · Conference Paper · Jun 2014
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