**2. Development and Implementation Challenges**

Electric demand forecasting is a daily procedure required for efficient and effective grid op‐ erations, North American Electric Reliability Corporation (NERC) compliance, and planning procedures. The current practice of making forecasts typically involves system operators manually generating the forecast using similar day-based methodologies. The forecast meth‐ ods vary depending on the individual operator, and hence are highly inconsistent. There is a tacit reluctance to embrace new technology among operators and the adoption of any new tools, such as decision support systems, would not happen unless they have passed strin‐ gent benchmarking criteria and scrutiny. The challenges in developing and implementing a new forecasting system are described in this section.

#### **2.1. Load Forecasting Overview**

Load forecasting is the science of predicting human energy usage in response to externali‐ ties. It is an essential procedure for effective and efficient real-time operations planning and control of generation within a power system. It provides the basis for unit-commitment and power system planning procedures (such as generation commitment schemes, contingency planning procedures, and temporary operating guidelines), maintenance scheduling, system security assessment (identifying stability concerns with unit-commitment and maintenance schemes), and trading schedules (market-posted generation/interconnection plans). It estab‐ lishes the generation, capacity, and spinning reserve schedules which are posted to the mar‐ ket. Without optimal load forecasts, additional expenses due to uneconomic dispatch, over/ under purchasing, and reliability uncertainty can cost a utility millions of dollars. A one per‐ cent reduction in load forecast uncertainty can mean the difference between forecasting an energy emergency or efficient system operation. Therefore a reduction in load forecast un‐ certainty provides considerable economic, reliability, and planning benefits [2].

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; and econometric data.

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


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 efficient demand forecasting [4].

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

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‐ ers is essential for providing accurate and reproducible load forecasts.

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 were acknowledged as high-priority issues.

#### **2.2. Existing Industry Model**

trial customers. Their demand profile conforms to seasonal and weather influences and,

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

Electric demand forecasting is a daily procedure required for efficient and effective grid op‐ erations, North American Electric Reliability Corporation (NERC) compliance, and planning procedures. The current practice of making forecasts typically involves system operators manually generating the forecast using similar day-based methodologies. The forecast meth‐ ods vary depending on the individual operator, and hence are highly inconsistent. There is a tacit reluctance to embrace new technology among operators and the adoption of any new tools, such as decision support systems, would not happen unless they have passed strin‐ gent benchmarking criteria and scrutiny. The challenges in developing and implementing a

Load forecasting is the science of predicting human energy usage in response to externali‐ ties. It is an essential procedure for effective and efficient real-time operations planning and control of generation within a power system. It provides the basis for unit-commitment and power system planning procedures (such as generation commitment schemes, contingency planning procedures, and temporary operating guidelines), maintenance scheduling, system security assessment (identifying stability concerns with unit-commitment and maintenance schemes), and trading schedules (market-posted generation/interconnection plans). It estab‐ lishes the generation, capacity, and spinning reserve schedules which are posted to the mar‐ ket. Without optimal load forecasts, additional expenses due to uneconomic dispatch, over/ under purchasing, and reliability uncertainty can cost a utility millions of dollars. A one per‐ cent reduction in load forecast uncertainty can mean the difference between forecasting an

thus, maybe referred to as conforming loads.

248 Decision Support Systems

conclusions on the models examined.

**2. Development and Implementation Challenges**

new forecasting system are described in this section.

**2.1. Load Forecasting Overview**

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‐ tors, engineers, and/or analysts. The model supports forecasting future demand by comparing the demand of historically similar days. It exploits common electric load perio‐ dicity of three fundamental frequencies: (1) diurnal, in which the minima are found in the early morning and midday and maxima at mid-morning and evening; (2) weekly, in which demand is lowest during the weekends and approximately the same from Tuesday to Thursday; and (3) seasonal, in which heating and cooling needs increase electrical demand during the winter and summer months [5]. Furthermore, holidays and special events are treated as aberrant occurrences and modelled separately. These periodic load behaviours were analyzed and provided the basis for a sequence of representative days, which are ad‐ justed for load growth and predicted weather phenomena.

against similar competing models. The power system supervisor selects the best model based on his or her subjective criteria, and the forecast is posted to the market. Figure 1 illustrates the procedure of STLF based on the traditional similar day model. The operator accesses the his‐ torical loads database, runs a pre-processing command to normalize the loads (e.g. by elimi‐ nating factors of load growth year-over-year), filters the dataset according to weather variables and load modifiers, and applies data transforms and regression analysis to the dataset filtered

Towards Developing a Decision Support System for Electricity Load Forecast

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

251

Grid personnel tend to be reluctant to adopt new tools or models due to the long-standing process for creation and evaluation of forecasts. It seems that a surprisingly high-degree of statistical accuracy and simulation evidence is required for grid personnel to consider imple‐ mentation of new models. Hence, a central challenge in developing a new load forecasting model is to supply substantial evidence to support its claims of accuracy, which is not limit‐ ed to model performance. In this chapter, we propose that empirical evidence to support the case for multi-region forecasts is essential for grid operations personnel to adopt new mod‐ elling techniques. This evidence for the weather and load diversity in the control area will be presented in sections 3 and 4, while section 7 will provide evidence in terms of assessment

Given its large geographic area, northerly latitude, and distance from any major body of wa‐ ter, the province of Saskatchewan is prone to considerable weather diversity. The province transitions its climates from humid continental in the south to subarctic in the north. Precipi‐ tation patterns vary considerably, typically decreasing from northeast to southwest. The

Wind chill statistics for each of the twelve load centres examined in this chapter were analyzed and compared. Regional weather data were recorded and analyzed from January 2005 to No‐ vember 2011. Sufficient weather diversity was identified within the analysis for all the load

by operator-selected input dates. Finally, the output forecast is produced.

**Figure 1.** Traditional Similar Day Procedure.

results on model performance for the case study.

summers are hot and dry while the winters are frigid.

**3. Analysis of Regional Weather**

The model consists of three components: base load, weather-influenced load, and special load. Base load considers the minimal load experienced throughout a day: an example of a base load application is lighting systems, whose usage is determined by the time of day. Weather-influenced load is the specific deviation from the averaged climactic conditions of the historically representative days. Common weather-influenced load typically pertains to heating and cooling devices such as furnaces and air conditioners. Special load includes the residual load use unaccounted for in the other load categories and is the most difficult to model. For example, special loads include the use of Christmas lights in winter or the outage of a major industrial customer. The weather-influenced loads usually rely on temperature variables, but a variety of forecast approaches exist from utility to utility [4].

Similar day models for load forecasting are often preferred for their simplicity. Operators are able to construct a forecast without a custom-made interface and manipulate data to ex‐ amine the sensitivity of the system to simulated changes in weather. The models tend to be intuitive to even the most novice operators and can be easily adjusted when unforeseen weather changes occur.

While simple and easy-to-construct, similar day models are often poor at reflecting diversity among regional load forecasts. The diversity of metering infrastructure complicates this model's accuracy as behind-the-meter generation often involves an aggregate estimate. This aggregate estimate ignores specific load details which may be unavailable. Furthermore, similar day models tend to use actual loads instead of weather-normalized loads; thereby reducing the ability of the model to predict electrical usage in a diverse weather environ‐ ment. An additional weakness of the approach is that load uncertainty is difficult to model. Uncertainty in metering cannot be assessed as system load is an aggregate calculation and masks individual metering errors.

Despite the massive advancements of technology, many power utilities have yet to embrace the new opportunities available in enhanced metering, process automation, and control schemes based on artificial intelligence techniques. Within the grid control centre, short term load forecasting remains a manual procedure, which makes use of similar day-based models for aggregate load forecasting. Every day the tagging desk operator manually prepares a dayahead forecast, which consists of hourly electric demand forecasts. The operator can use weather forecasts for multiple regions, or forecast as many as four major load regions to pro‐ duce an aggregate system load. When a complete forecast has been generated, it is compared against similar competing models. The power system supervisor selects the best model based on his or her subjective criteria, and the forecast is posted to the market. Figure 1 illustrates the procedure of STLF based on the traditional similar day model. The operator accesses the his‐ torical loads database, runs a pre-processing command to normalize the loads (e.g. by elimi‐ nating factors of load growth year-over-year), filters the dataset according to weather variables and load modifiers, and applies data transforms and regression analysis to the dataset filtered by operator-selected input dates. Finally, the output forecast is produced.

**Figure 1.** Traditional Similar Day Procedure.

tors, engineers, and/or analysts. The model supports forecasting future demand by comparing the demand of historically similar days. It exploits common electric load perio‐ dicity of three fundamental frequencies: (1) diurnal, in which the minima are found in the early morning and midday and maxima at mid-morning and evening; (2) weekly, in which demand is lowest during the weekends and approximately the same from Tuesday to Thursday; and (3) seasonal, in which heating and cooling needs increase electrical demand during the winter and summer months [5]. Furthermore, holidays and special events are treated as aberrant occurrences and modelled separately. These periodic load behaviours were analyzed and provided the basis for a sequence of representative days, which are ad‐

The model consists of three components: base load, weather-influenced load, and special load. Base load considers the minimal load experienced throughout a day: an example of a base load application is lighting systems, whose usage is determined by the time of day. Weather-influenced load is the specific deviation from the averaged climactic conditions of the historically representative days. Common weather-influenced load typically pertains to heating and cooling devices such as furnaces and air conditioners. Special load includes the residual load use unaccounted for in the other load categories and is the most difficult to model. For example, special loads include the use of Christmas lights in winter or the outage of a major industrial customer. The weather-influenced loads usually rely on temperature

Similar day models for load forecasting are often preferred for their simplicity. Operators are able to construct a forecast without a custom-made interface and manipulate data to ex‐ amine the sensitivity of the system to simulated changes in weather. The models tend to be intuitive to even the most novice operators and can be easily adjusted when unforeseen

While simple and easy-to-construct, similar day models are often poor at reflecting diversity among regional load forecasts. The diversity of metering infrastructure complicates this model's accuracy as behind-the-meter generation often involves an aggregate estimate. This aggregate estimate ignores specific load details which may be unavailable. Furthermore, similar day models tend to use actual loads instead of weather-normalized loads; thereby reducing the ability of the model to predict electrical usage in a diverse weather environ‐ ment. An additional weakness of the approach is that load uncertainty is difficult to model. Uncertainty in metering cannot be assessed as system load is an aggregate calculation and

Despite the massive advancements of technology, many power utilities have yet to embrace the new opportunities available in enhanced metering, process automation, and control schemes based on artificial intelligence techniques. Within the grid control centre, short term load forecasting remains a manual procedure, which makes use of similar day-based models for aggregate load forecasting. Every day the tagging desk operator manually prepares a dayahead forecast, which consists of hourly electric demand forecasts. The operator can use weather forecasts for multiple regions, or forecast as many as four major load regions to pro‐ duce an aggregate system load. When a complete forecast has been generated, it is compared

variables, but a variety of forecast approaches exist from utility to utility [4].

justed for load growth and predicted weather phenomena.

weather changes occur.

250 Decision Support Systems

masks individual metering errors.

Grid personnel tend to be reluctant to adopt new tools or models due to the long-standing process for creation and evaluation of forecasts. It seems that a surprisingly high-degree of statistical accuracy and simulation evidence is required for grid personnel to consider imple‐ mentation of new models. Hence, a central challenge in developing a new load forecasting model is to supply substantial evidence to support its claims of accuracy, which is not limit‐ ed to model performance. In this chapter, we propose that empirical evidence to support the case for multi-region forecasts is essential for grid operations personnel to adopt new mod‐ elling techniques. This evidence for the weather and load diversity in the control area will be presented in sections 3 and 4, while section 7 will provide evidence in terms of assessment results on model performance for the case study.
