Overview of Load Forecasting Techniques

Load forecasting is an important tool used to estimate the future energy or power consumption and in general aims to minimise utility risks and power costs in power planning, operation, and traditional power control [60] [61]. An accurate forecast is the basis for reducing operational costs and using different energy sources to create a secure power system. Furthermore, accurate forecasts are an effective tool for energy management system problems such as load shedding, peak demand reduction and electrical infrastructure development by providing the necessary information for making informed decisions. Electrical load forecasting is a complex procedure, due to the volatility and potential number of factors affecting the forecast model accuracy. The historical load values, weather factors (temperature, humidity and wind speed), season, economic situation and demographic data are some of the major factors considered in most of the load forecasting models [61] [62]. This section will introduce and compare a selection of load forecasting methods that have been used to predict electrical and peak demand, especially for highly volatile load situations. Finally, the literature will be reviewed to present the role of load forecasting in low voltage network applications and how they are used with an optimal controller to increase energy savings. Load forecasting algorithms have been discussed and investigated extensively in the recent literature, for example [60] [61]. These researchers divided load forecasts into four different categories based on the prediction horizon as described in Table 2-1.

Table 2-1:Categories of forecasts based on the prediction horizon.

Forecast categories Explanation

Very short-term forecasts From a few minutes to one hour ahead. Short-term forecasts From one hour to several days ahead. Medium term forecasts From one week to one year ahead.

Long-term forecasts From one year or more ahead.

In power system applications, short-term load forecasting has been used widely for operation scheduling, power system stability and economic operations. A large variety of methodologies

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and models have been employed in order to achieve an accurate load forecast. These models are mainly divided into three main technologies: traditional or statistical methods, artificial intelligence methods and hybrid systems [62] [63], as shown in Figure 2-3. Figure 2-3 shows the spectrum of short-term load forecasting methods categorised according to the three main technologies based on a literature review in this work and the study by Papaioannou et al. [62]. The procedure of designing a forecasting model does not usually use a single-pass. It may be required to visit previous steps before finalising the model, especially between the training model, model parameters and variable selection steps. Therefore, it will require to split the data set into training, validation, and testing sets. In general, the training set is used to train the model and find the demand patterns and model parameters and the validation set is used to find the best model. The data size should give a trade-off between finding the best model parameters and model accuracy and avoid overfitting.

Methods of short term load forecasting

Traditional or statistical methods

Artificial intelligence

methods Hybrid system

• ARIMA • ARIMAX • SARIMA • AR • ARMA • Regression • The naïve method • Similar day approach

• ANN • SVM • Fuzzy logic • Expert system • Random forest • Probabilistic forecast

• ARIMA and ANFIS • ARMAX and SVM • ARIMAX and ANN

Figure 2-3: The spectrum of short-term forecasting methods according to the three main technologies.

2.3.1.1 Time or statistical series forecast techniques

These techniques are prediction methods that aim to present the data as a function of time and often used to forecast electrical load demand. In general, the traditional methods such as the naïve method [61] and linear and multilinear regression methods [60] may have problems modelling complex nonlinear time series [63]. However, the statistical methods are simple and easy to implement compared to other categories. Time series models have been developed based on the analysis of seasonal trends or patterns to forecast the time objectives ahead in many research areas such as weather, rainfall [64] [65], electric load demand [66] [67] [68],

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and price forecasting [69] [70]. Generally, time series approaches include the following techniques: Auto Regressive (AR), Moving Average (MA) and Auto Regressive Moving Average (ARMA), and linear regression methods. The Auto Regressive Integrated Moving Average with exogenous variables (ARIMAX) method is a nonlinear model with the ability to work for linear models as well [67] [71]. Usually, ARIMA models are referred to as Box- Jenkins models due to ARIMA being more popular after 1970 when Box and Jenkins applied this method in their studies [72] [73]. ARIMA and ARIMAX models are two of the most popular time series models and have been widely used to forecast electrical load demand and other research areas that have highly stochastic load behaviour [67] [71]. However, using ARIMA and ARIMAX load forecasting models for RTG cranes and port substations has not been previously reported and investigated in the literature.

2.3.1.2 Artificial intelligence methods

The artificial intelligent forecast methods are mainly used to model complex and unknown nonlinear relationships for example the Artificial Neural Networks (ANN) model does not require any predetermined functional relationship between load and predictor variables [63] [74]. However, many of the intelligence forecast techniques such as ANN are described as black-box due to the lack of transparency of the results or models. The intelligence methods include mainly ANN [66], fuzzy logic methods [75] and support vector regression [76]. Generally, electrical load time series show stochastic and volatile behaviour in the low voltage distribution side of power networks. The nonlinear system of load behaviour, which has many exogenous variables such as temperature, wind speed and time period of-the-day, make short- term load forecasting more complicated [77] [78]. In order to solve these complex relationship and increase forecasting accuracy, many ANN models have been developed to predict LV demand [79] [80]. ANN forecasting models have been discussed and developed for a wide range of LV network applications such as microgrid systems and buildings that have a highly volatile and stochastic load behavior, as will be discussed in the following section. ANN techniques to forecast RTG crane and port substation demands have not been previously reported in the literature.

2.3.1.3 The hybrid forecast models

Recently, hybrid systems have been applied to increase forecast accuracy by combining two or more methods. Hybrid systems have mainly been developed to utilise the advantages of

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each approach and ameliorate any weakness or disadvantages of any single method [60] [81]. The hybrid model may combine statistical methods and artificial intelligence methods such as ARIMAX and ANN [66], statistical techniques for pre-processing of data, neural network and price spike detection [81], Wavelet Transform (WT) and Least Squares Support Vector Machines (LSSVM) [82]. In order to compare the main short-term load forecasting methods and techniques, Table 2-2 illustrates the advantages and drawbacks of the main short-term load forecasting models based on the literature reviewed in the previous sections and [83] [84] [85]. In addition, ARIMAX and ANN forecasting models for Low voltage network demand such as Electric Vehicles (EVs), building and industrial load for peak shaving and energy cost saving will be discussed in more detail in Section 2.3.2.

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Table 2-2: The advantages and drawbacks of some of the main short-term forecasting methods.

Model Advantages Drawbacks

Linear regression • Simple method and easy to use because this method analyses the basic

relationship between two sets of data.

• Analyses only the linear relationship between two sets of data.

• Assumes that the historical linear relationship will continue in the future. ARIMAX • Widely used to forecast electrical

demand which gives sufficient information about how this method can be used.

• The exogenous variables help to decrease the forecast model error.

• Historical data is the main source of data for ARIMAX as a time series method. • Require extensive data analysis and large

amount of data to determine the seasonal patterns.

ANN • Does not require any functional relationship between the load and predictor variables.

• Able to detect complex nonlinear relationships.

• More flexible.

• ANN has been described as a “black box” because it is difficult to explain the model.

• Increasing the number of layers in an ANN model will lead to increased costs of the model and training time.

• Tends to overfit and it requires large amounts of data to determine the model parameters.

• Difficult to generalise the forecast model and identify the best model parameters. SVM • This method avoids overfitting.

• This method provides expert knowledge about the forecast target.

• Choosing the correct kernel is one of the main difficulties and limitations of this method.

ANFIS • The adaptability of algorithms in this model help to avoid the fuzzy system problem by eliminating the need to generate a set of fuzzy rules and use ANN to create an automatic fuzzy rule generation.

• This model is very sensitive to a number of fuzzy rules and the model’s complexity will increase when the number of fuzzy rules increase.

• This model described as “black box” because it is difficult to understand the nature of the ANN model.

• High complexity and it requires large amounts of data to determine the model parameters.

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