Mashud Rana

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We present new approaches for building yearly and seasonal models for 5-minute ahead electricity load forecasting. They are evaluated using two full years of Australian electricity load data. We first analyze the cyclic nature of the electricity load and show that the autocorrelation function captures these patterns and can be used to extract useful(More)
Appropriate feature (variable) selection is crucial for accurate forecasting. In this paper we consider the task of forecasting the future electricity load from a time series of previous electricity loads, recorded every 5 minutes. We propose a two-step approach that identifies a set of candidate features based on the data characteristics and then selects a(More)
We present PSF-NN, a new approach for time series forecasting. It combines prediction based on sequence similarity with neural networks. PSF-NN first generates predictions using the PSF algorithm that are then refined by the neural network component, which also utilizes additional features. We evaluate the performance of PSF-NN using a time series of hourly(More)
Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that(More)
We present a new approach for building weekday-based prediction models for electricity load forecasting. The key idea is to conduct a local feature selection using autocorrelation analysis for each day of the week and build a separate prediction model using linear regression and backpropagation neural networks. We used two years of 5-minute electricity load(More)
We present a new approach for electricity load forecasting based on non-decimated multilevel wavelet transform, in combination with two-stage feature selection and machine learning prediction algorithm. The key idea is to decompose the non-stationary and noisy electricity load data into sub-series of different frequencies, analyse and predict them(More)
We present INN, a new approach for predicting the hourly electricity load profile for the next day from a time series of previous electricity loads. It uses an iterative methodology to make the predictions for the 24-hour forecasting horizon. INN combines an efficient mutual information feature selection method with a neural network forecasting algorithm.(More)
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on(More)
Forecasting solar power generated from photovoltaic systems at different time intervals is necessary for ensuring reliable and economic operation of the electricity grid. In this paper, we study the application of neural networks for predicting the next day photovoltaic power outputs in 30 minutes intervals from the previous values, without using any(More)