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Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation...
Short term load forecasting (STLF) is an important part of generation scheduling and optimal energy management. Small island power systems, however, often have consumers that individually represent a relatively large percentage of the systems overall load and need to specifically forecast these individual loads. This paper, using data from two large industries in Trinidad and Tobago, evaluates the...
This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine...
The paper presents a locally recurrent Fuzzy neural architecture to forecast electrical loads in an energy market on a short-term basis. In recent years combination of recurrent filter neurons with Fuzzy neural networks has gained significance to provide the identification of the temporal nature of the time series data. Further to increase the dimension of the input space the consequent part of the...
This paper proposes a hybrid short-term load forecasting (STLF) framework with a new, more efficient, input selection method. Correlation analysis and ℓ2-norm are used in combination to select suitable inputs to individual Bayesian neural networks (BNNs), which are used to forecast the load. Forecast outputs are then weighted using calculated weighting coefficients and summed to obtain the final forecast...
Aim of this work is to develop a novel hybrid model for the day-ahead prediction of a distribution substation feeder load. The model comprises an unsupervised machine learning stage, where a clustering of daily load curves takes place, and an Artificial Neural Network (ANN), based on the clustering output, which performs the prediction. The model is tested on the day-ahead prediction of a complete...
Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. In this paper, we will discuss night peak load forecasting of Algerian power system using time series back propagation neural networks, including the effect of the temperature, working days and weekends.
Microgrids are a rapidly growing sector of smart grids, which will be an essential component in the trend toward distributed electricity generation. In the operation of a microgrid, forecasting the short-term load is an important task. With a more accurate short-term loaf forecast (STLF), the microgrid can enhance the management of its renewable and conventional resources and improve the economics...
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