**1. Introduction**

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246 Decision Support Systems

York, EUA.

gy, Helsinki, Finland; (2007).

Short-term load forecasting (STLF) is an essential procedure for effective and efficient realtime operations planning and control of generation within a power system. It provides the basis for unit-commitment and power system planning procedures, maintenance schedul‐ ing, system security assessment, and trading schedules. It establishes the generation, capaci‐ ty, and spinning reserve schedules which are posted to the market. Without optimal load forecasts, additional expenses due to uneconomic dispatch, over/under purchasing, and reli‐ ability uncertainty can cost a utility millions of dollars [1].

Many approaches have been considered for STLF. The benefits of increased computational power and data storage have enhanced the capabilities of artificial intelligence methods for da‐ ta analysis within the power industry [1]. Yet, even with the advancements of technology, in‐ dustry forecasts are often based on a traditional similar day forecasting methodology or rigid statistical models with reduced variable modifiers for forecasting aggregated system load.

Power systems with a large spatial presence provide an increased challenge for load fore‐ casters, as they often face large diversity within their load centres as well as diverse weather conditions. These geographically separated load centres often behave independently and add considerable complexity to the system dynamics and forecasting procedure.

This chapter investigates forecasting of electrical demand at an electric utility within the province of Saskatchewan, Canada and proposes a multi-region load forecasting system based on weather-related demand variables. The control area examined consists of over 157,000 kilometres of power lines with transmission voltages of 72, 138, and 230KV. The control area was apportioned into twelve load centres, consisting primarily of conforming loads. These conforming loads were cities and rural load clusters not including large indus‐

trial customers. Their demand profile conforms to seasonal and weather influences and, thus, maybe referred to as conforming loads.

energy emergency or efficient system operation. Therefore a reduction in load forecast un‐

Towards Developing a Decision Support System for Electricity Load Forecast

http://dx.doi.org/10.5772/51306

249

Electric load is the demand for electricity by a population, which results from cultural and economic biases and is influenced by externalities [3]. Common drivers for electric loads include: end use relationships (appliances, industries, etc.); time of day; weather;

The accuracy of the predicted demand can impact a variety of power systems operations,

**•** The dispatch plan of generation units may not be optimal, resulting in economic losses.

**•** Maintenance scheduling may suffer missed opportunities for preventative maintenance.

Since electrical energy cannot yet be efficiently stored in bulk quantities, reliable forecasts are essential to provide efficient scheduling for an electric utility. The increasing regulatory presence in the electricity industry places increased importance on the need for accurate and

Load forecasting is traditionally divided into three categories: long term forecasts, predict‐ ing several months to several years into the future; medium term forecasts, predicting one or more weeks into the future; and short term forecasts, predicting several minutes to one

Short term load forecasting is conducted not only for efficient operations and planning, but also to comply with regulations imposed by NERC. These forecasts predict either power de‐ mand, for real-time forecasting or peak forecasting in megawatts, or energy demand, for hourly or daily forecasting in megawatt-hours. Regardless of the class of load forecasting model utilized, understanding the relationship between electric demand and forecast driv‐

Recent findings from NERC's Load Forecasting Working Group have identified substantial inconsistencies in forecasting methodologies such that the reported data are not comparable [4]. While it is difficult to standardize forecasting methods across all regions, NERC has en‐ couraged the collection and reporting of load data to include greater detail with respect to demand-side management in terms of regional diversity factors and non-member loads in forecasts. These suggestions indicated weaknesses in current practices of load forecast re‐ porting, specifically, consideration for regional diversity and mixed aggregation methods

Within the utility examined, load forecasting has been conducted based on a similar day ag‐ gregate load model, which was constructed based on expert knowledge elicited from opera‐

**•** Energy trading schedules may miss advantageous purchasing or selling options.

**•** System security may misidentify system stability on prospective generation plans.

week into the future. The focus of this chapter is on short term load forecasting.

ers is essential for providing accurate and reproducible load forecasts.

certainty provides considerable economic, reliability, and planning benefits [2].

and econometric data.

efficient demand forecasting [4].

were acknowledged as high-priority issues.

**2.2. Existing Industry Model**

such as:

The chapter is organized as follows: Section 2 introduces the current load forecasting system used at this utility and describes challenges associated with developing a new decision sup‐ port system. Section 3 discusses the weather diversity of the load centres and the loadweather patterns observed. Section 4 presents the load diversity analysis of the load centres. Section 5 identifies the methodology of the research as well as the load forecasting models examined, which consist of (1) a similar day aggregate model developed in conjunction with the utility's load forecasting experts, (2) an ANN aggregate model, and (3) an ANN multiregion model. Section 6 describes the modelling processes and the performance evaluation methods. Section 7 presents the case study of predicting the hourly energy consumption throughout the 2011 year. The predicted results generated from each of the three models are also presented, which demonstrate the superior performance of the proposed multi-region load forecasting system over the aggregate load forecasting models. Section 8 presents some conclusions on the models examined.
