Integrating long-term economic scenarios into peak load forecasting: An application to Spain

The treatment of trend components in electricity demand is critical for long-term peak load forecasting. When forecasting high frequency variables, like daily or hourly loads, a typical problem is how to make long-term scenarios - regarding demographics, GDP growth, etc. - compatible with short-term projections. Traditional procedures that apply de-trending methods are unable to simulate forecasts under alternative long-term scenarios. On the other hand, existing models that allow for changes in long-term trends tend to be characterized by end-of-year discontinuities. In this paper a novel forecasting procedure is presented that improves upon these approaches and is able to combine long and short-term features by employing temporal disaggregation techniques. This method is applied to forecast electricity load for Spain and its performance is compared to that of a nonlinear autoregressive neural network with exogenous inputs. Our proposed procedure is flexible enough to be applied to different scenarios based on alternative assumptions regarding both long-term trends as well as short-term projection

Dynamic Bayesian smooth transition autoregressive (DBSTAR) models for non-stationary nonlinear time series

In this thesis, Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models are proposed for nonlinear time series, as an alternative to both the classical Smooth Transition Autoregressive (STAR) models and Computational Bayesian STAR (CBSTAR) models. DBSTAR models are autoregressive formulations of dynamic linear models based on polynomial approximations of transition functions of STAR models. Unlike classical STAR and CBSTAR models, their parameters vary in time, being suitable for modelling both global and local non-stationary processes. Since DBSTAR models are Bayesian, the models do not require extensive historical data for parametric estimation and allow expert intervention via prior distribution assessment of model parameters. Because they are analytical and sequential, DBSTAR models, respectively, avoid potential computational problems associated with CBSTAR models, such as convergence issues, and allow fast estimation of dynamic parameters sequentially in time, being thus suitable for real time applications. Proposed DBSTAR models have been applied to two data sets: the much used Canadian Lynx data set, in which the aim is to validate DBSTAR models by comparing their fitting performances with existing approaches in the literature, and a Brazilian electricity load data set, for which existing models are not suitable

Density forecasting for long-term electricity demand in South Africa using quantile regression

Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed.   Aim: The aim of the study was: (1) to apply a quantile regression (QR) model to forecast hourly distribution of electricity demand in South Africa; (2) to investigate variabilities in the forecasts and evaluate uncertainties around point forecasts and (3) to determine whether the future peak electricity demands are likely to increase or decrease.   Setting: The study explored the probabilistic forecasting of electricity demand in South Africa.   Methods: The future hourly electricity demands were forecasted at 0.01, 0.02, 0.03, … , 0.99 quantiles of the distribution using QR, hence each hour of the day would have 99 forecasted future hourly demands, instead of forecasting just a single overall hourly demand as in the case of OLS.   Results: The findings are that the future distributions of hourly demands and peak daily demands would be more likely to shift towards lower demands over the years until 2023 and that QR gives accurate long-term point forecasts with the peak demands well forecasted.   Conclusion: QR gives forecasts at all percentiles of the distribution, allowing the potential variabilities in the forecasts to be evaluated by comparing the 50th percentile forecasts with the forecasts at other percentiles. Additional planning information, such as expected pattern shifts and probable peak values, could also be obtained from the forecasts produced by the QR model, while such information would not easily be obtained from other forecasting approaches. The forecasted electricity demand distribution closely matched the actual demand distribution between 2012 and 2015. Therefore, the forecasted demand distribution is expected to continue representing the actual demand distribution until 2023. Using a QR approach to obtain long-term forecasts of hourly load profile patterns is, therefore, recommended

Probabilistic load forecasting for the low voltage network : forecast fusion and daily peaks

Short-term forecasts of energy consumption are invaluable for operation of energy systems, including low voltage electricity networks. However, network loads are challenging to predict when highly desegregated to small numbers of customers, which may be dominated by individual behaviours rather than the smooth profiles associated with aggregate consumption. Furthermore, distribution networks are challenged almost entirely by peak loads, and tasks such as scheduling storage and/or demand flexibility maybe be driven by predicted peak demand, a feature that is often poorly characterised by general-purpose forecasting methods. Here we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure for combining conventional and peak forecasts to produce a general-purpose probabilistic forecast with improved performance during peaks. The proposed approach is demonstrated using real smart meter data and a hypothetical low voltage network hierarchy comprising feeders, secondary and primary substations. Fusing state-of-the-art probabilistic load forecasts with peak forecasts is found to improve performance overall, particularly at smart-meter and feeder levels and during peak hours, where improvement in terms of CRPS exceeds 10%