This paper describes three software applications for distribution network load forecasting in a Smart Grid environment: (i) short-term feeder load forecasting, (ii) shortterm substation transformer load forecasting and transformer rating, and (iii) next-year load pocket forecasting. The shortterm feeder load forecasting allows a utility to reduce the possibility of feeder overloading. The subst...

LONG-TERM GOALS Using a combination of theoretical, observational, and high-order model resources, we aim to develop and improve the modelling and forecasting capabilities for crucial problems such as long range weather forecasting of planetary scale convection patterns in the tropics and short term climate change in the polar regions such as sea ice reemergence. OBJECTIVES DISTRIBUTION STATEME...

Current methodologies for the optimal operation of district heating systems use model predictive control. Accurate forecasting of the water temperature at critical points is crucial for meeting constraints related to consumers while minimizing the production costs for the heat supplier. A new forecasting methodology based on conditional Finite Impulse Response (cFIR) models is introduced, for w...

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to reso...

The article is devoted to investigating the application of aggregating algorithms to the problem of the long-term forecasting. We examine the classic aggregating algorithms based on the exponential reweighing. For the general Vovk’s aggregating algorithm we provide its generalization for the long-term forecasting. For the special basic case of Vovk’s algorithm we provide its two modifications f...

Forecasting plays an important role in business, technology, climate and many others. As an example, effective forecasting can enable an organization to reduce lost sales, increase profits and more efficient production planning. In this paper, we present a parallel algorithm for short term forecasting based on a time series model called weighted moving average. Our algorithm is mapped on OTIS-m...

This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expe...

— In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peakin...

Abstract. A body of psychological research has examined the correspondence between a judge’s subjective probability of an event’s outcome and the event’s actual outcome. The research generally shows that subjective probabilities are noisy and do not match the “true” probabilities. However, subjective probabilities are still useful for forecasting purposes if they bear some relationship to true ...

The Double Vector Quantization method, a long-term forecasting method based on the SOM algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the Dou...