Forecasting electricity demand using generalized long memory

Soares, Lacir J.; Souza, Leonardo Rocha

doi:10.1016/j.ijforecast.2005.09.004

Cited by 74 publications

(42 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we have shown in a previous work, in our problem scenario this can be avoided easily by using the work schedule calender. A similar concept has been applied in [27] (one model for each type of day) and [28] (one model for each hour of the day) to STLF and in [23] to building STLF.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting in air-conditioned non-residential Buildings

Penya

Borges

Agote

et al. 2011

2011 IEEE International Symposium on Industrial Electronics

View full text Add to dashboard Cite

Abstract-Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been classically object of vast research since energy load prediction is known to be nonlinear. In a previous work, we focused on non-residential building STLF, an special case of STLF where weather has negligible influence on the load. Now we tackle more modern buildings in which the temperature does alter its energy consumption. This is, we address here fully-HVAC (Heating, Ventilating, and Air Conditioning) ones. Still, in this problem domain, the forecasting method selected must be simple, without tedious trial-and-error configuring or parametrising procedures, work with scarce (or any) training data and be able to predict an evolving demand curve. Following our preceding research, we have avoided the inherent non-linearity by using the work day schedule as day-type classifier. We have evaluated the most popular STLF systems in the literature, namely ARIMA (autoregressive integrated moving average) time series and Neural networks (NN), together with an Autoregressive Model (AR) time series and a Bayesian network (BN), concluding that the autoregressive time series outperforms its counterparts and suffices to fulfil the addressed requirements, even in a 6 day-ahead horizon.

show abstract

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting in air-conditioned non-residential Buildings

Penya

Borges

Agote

et al. 2011

2011 IEEE International Symposium on Industrial Electronics

View full text Add to dashboard Cite

show abstract

“…Bruhns, Deurveilher & Roy (2005) gave a detailed description of a non-linear forecasting model of French load in use at Electricite de France (EDF), also allowing for different levels of seasonality and weather dependence. In this paper, we present a different multiple-equation linear time-varying regression model for French national hourly electricity load, with one equation for each hour, like the approach of Ramanathan et al (1997), and more recently, Soares & Souza (2006). We do not include periodic seasonal ARIMA components as they are difficult to interpret from an economic point of view.…”

Section: Introductionmentioning

confidence: 99%

An hourly periodic state space model for modelling French national electricity load

Dordonnat

Koopman

Ooms

et al. 2008

International Journal of Forecasting

110

View full text Add to dashboard Cite

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be readily estimated including yearly patterns and their time-varying nature. The empirical analysis involves an out-ofsample forecasting assessment up to seven days ahead. The one-day ahead forecasts from fourty-eight bivariate models are compared with twenty-four univariate models for all hours of the day. We find that the implied forecasting function strongly depends on the hour of the day.

show abstract

“…In addition, [8] used the temperature data in a feedback NN obtaining a remarkable Mean Absolute Percentage Error (MAPE) of 1.945% (Section 4.3 details the mathematical definition of this type of error measure), but this result was obtained measuring only a single week in a whole year, which is not statistically representative. Finally, all artificial intelligence methods waste most of their efforts in modelling non-linear behaviour of the work calendar [16,17].…”

Section: Related Workmentioning

confidence: 99%

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

et al. 2013

View full text Add to dashboard Cite

Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

show abstract

Forecasting electricity demand using generalized long memory

Cited by 74 publications

References 22 publications

Short-term load forecasting in air-conditioned non-residential Buildings

Short-term load forecasting in air-conditioned non-residential Buildings

An hourly periodic state space model for modelling French national electricity load

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

Contact Info

Product

Resources

About