61,435 research outputs found
Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model
Long-term load forecasting plays a vital role for utilities and planners in
terms of grid development and expansion planning. An overestimate of long-term
electricity load will result in substantial wasted investment in the
construction of excess power facilities, while an underestimate of future load
will result in insufficient generation and unmet demand. This paper presents
first-of-its-kind approach to use multiplicative error model (MEM) in
forecasting load for long-term horizon. MEM originates from the structure of
autoregressive conditional heteroscedasticity (ARCH) model where conditional
variance is dynamically parameterized and it multiplicatively interacts with an
innovation term of time-series. Historical load data, accessed from a U.S.
regional transmission operator, and recession data for years 1993-2016 is used
in this study. The superiority of considering volatility is proven by
out-of-sample forecast results as well as directional accuracy during the great
economic recession of 2008. To incorporate future volatility, backtesting of
MEM model is performed. Two performance indicators used to assess the proposed
model are mean absolute percentage error (for both in-sample model fit and
out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)
A comparison of univariate methods for forecasting electricity demand up to a day ahead
This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives
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