Short term load forecasting and the effect of temperature at the low voltage level

Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required for optimising a wide range of potential network solutions on the low voltage (LV) grid, including the integration of low carbon technologies (such as photovoltaics) and the utilisation of battery storage devices. Despite the need for accurate LV level load forecasts, much of the literature has focused on the individual household or building level using data from smart meters, or on aggregates of such data. This study provides a detailed analysis of several state-of-the-art methods for both point and probabilistic LV load forecasts. We evaluate the out-of-sample forecast accuracies of these methodologies on 100 real LV feeders, for horizons from one to four days ahead. In addition, we also test the effect of the temperature (both actual and forecast) on the accuracy of load forecasts. We present some important results on the drivers of forecasts accuracy as well as on the empirical comparison of point and probabilistic forecast measures.     

Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.