24-Hour Electrical Load Data - A Time Series or a Set of Independent Points?

The paper investigates whether a time series or a set of independent points is a more appropriate description of 24-hour Irish electrical load data. A set of independent points means that the load at each hour of the day is independent from the load at any other hour. The data is first split into 24 series, one for each hour of the day i.e. a 1am 2am 3am series etc. These are called parallel series. The linear cross-correlation's of the parallel series are used to indicate independence. While the loads at 9am and 6pm to 8pm appear independent the remaining loads are highly inter-correlated. This suggests that 24-hour electrical load data has a dual nature. Two techniques are used to test this hypothesis. The first technique models each parallel series using neural networks. This technique is found to be computationally expensive. The second technique uses a hybrid technique called the Multi Time Scale (MTS) technique. This models 24-hour electrical load data as a time series that can be adjusted by 5 parallel forecasts and a daily cumulative model. The results show that the MTS forecasts are superior to the parallel forecasts except for 9am and 6pm to 8pm. A composite model using neural networks for 9am and 6pm to 8pm and the MTS model elsewhere takes advantage of the dual nature of the data reducing error and computational expense

A strategy for short-term load forecasting in Ireland

Electric utilities require short-term forecasts of electricity demand (load) in order to schedule generating plant up to several days ahead on an hourly basis. Errors in the forecasts may lead to generation plant operation that is not required or sub-optimal scheduling of generation plants. In addition, with the introduction of the Electricity Regulation Act 1999, a deregulated market structure has been introduced, adding increased impetus to reducing forecast error and the associated costs. This thesis presents a strategy for reducing costs from electrical demand forecast error using models designed specifically for the Irish system. The differences in short-term load forecasting models are examined under three independent categories: how the data is segmented prior to modelling, the modelling technique and the approach taken to minimise the effect of weather forecast errors present in weather inputs to the load forecasting models. A novel approach is presented to determine whether the data should be segmented by hour of the day prior to modelling. Several segmentation strategies are analysed and the one appropriate for Irish data identified. Furthermore, both linear and nonlinear techniques are compared with a view to evaluating the optimal model type. The effect of weather forecast errors on load forecasting models, though significant, has largely been ignored in the literature. Thus, the underlying issues are examined and a novel method is presented which minimises the effect of weather forecast errors

Time series forecasting methodologies for electricity supply systems

Forecasting is an essential function in the electricity supply industry. Electricity demand forecasting is performed on number of different time-scales depending on the function for which they are required. In the short term (hourly) forecasts of electricity demand are required for the safe and efficient operation of the power system. Medium term forecasts (weekly) are needed for economic planning and long term (yearly) forecasts are required for deciding on system generation and transmission expansion plans. In recent years the electricity supply industry in some countries has undergone significant changes mainly due to a levelling off in the growth of electricity demand and also due to technological advances. There has been a move toward the existence of a number of smaller generating companies and the emergence of a competitors market has resulted. These changes in the structure of the industry have led to new requirements in the area of forecasting, where forecasts are now required on a small time-scale over a longer forecasting horizon, for example, the production of hourly forecasts over a period of a month. The thesis presents a novel approach to the solution of the production of short term forecasts over a relatively long term forecast horizon. The mathematical formulation of the technique is presented and an application procedure is developed. Two applications of the technique are given and the issues involved in the implementation investigated. In addition, the production of weekly electricity demand forecasts using the optimal form of the available weather variables is investigated. The value of using such a variable in cases where it is not a dominant influencing factor in the system is assessed. The application of neural networks to the problem of weekly electricity demand forecasting is examined. Neural networks are also applied to the problem of the production of both aggregate and disaggregate electricity sales forecasts for up to five years in advance. Conclusions regarding the methodologies presented in the thesis are drawn and directions for future works are considered

Integration of multi-time-scale models in time series forecasting

A solution to the problem of producing long-range forecasts on a short sampling interval is proposed. It involves the incorporation of information from a long sampling interval series, which could come from an independent source, into forecasts produced by a state-space model based on a short sampling interval. The solution is motivated by the desire to incorporate yearly electricity consumption information into weekly electricity consumption forecasts. The weekly electricity consumption forecasts are produced by a state-space structural time series model. It is shown that the forecasts produced by the forecasting model based on weekly data can be improved by the incorporation of longer-tim e-scale information, particularly when the forecast horizon is increased from 1 year to 3 years. A further example is used to demonstrate the approach, where yearly UK primary fuel consumption information is incorporated into quarterly fuel consumption forecasts