The main purpose of the thesis was to investigate application of various **computational** **intelligence** **methods** for **short** **term** **load** **forecasting**. In Chapter 3, STLF by three types of neural structures i.e. MLPNN, RNN and FLANN **using** back propagation training algorithm was discusses and their suitability for STLF was investigated. FLANN was modeled **using** Chebyshev, trigonometric, and algebraic expansion of input vector. RNN as proposed by Savaran [69] was modified to obtain better MAPE. Chapter 4 discusses about suitability of GA, PSO, and AIS as training algorithms for MLPNN and the corresponding suitability for STLF was investigated. GA and AIS were hybridized with back propagation to train the same neural structure and were found to be achieving better MAPE than utilizing only GA or PSO. This chapter summarizes the work reported in this thesis, specifying the limitations of the study and provides some indications for future work. Following this introduction section 5.2 lists the achievements from the work. Section 5.3 provides the limitations and section 5.4 presents indications toward future work.

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applications. **Load** **forecasting** depends on operating time horizon of **load** occurrence. Accordingly, **load** **forecasting** is classified in three categories such as **short** **term** **load** **forecasting**, medium **term** **load** **forecasting**, and long **term** **load** **forecasting** [6]. **Short** **term** **load** forecast is applicable for **load** forecasts within one day to one week ahead of **load** occurrence, while medium **term** **load** forecast is applicable for **forecasting** loads in a period within one week to one month ahead of **load** occurrence. For each classification there are appropriate **methods** for **load** **forecasting**. Here, we are going to show the impact of ANN and FL in **short** **term** **load** **forecasting**. This paper attempts to compare two new techniques for **short** **term** **load** **forecasting**. The paper is organized as follows: In sections 2 and 3 the mathematical models for **short** **term** **load** **forecasting** with FL and ANN are proposed. In section 4 a case study to show the application of two **methods** is represented and compare between **short**-**term** and medium-**term** LF with ANN, finally in section 5 provided the conclusion.

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Typically, an MPC-based EMS requires **load** forecasts with a prediction horizon of up to a few days, which in the literature is often referred to as **short**-**term** **load** **forecasting** (STLF). Especially for the **forecasting** of the electric **load** of large territories, various approaches have been proposed for that purpose [2]. In general, these approaches are divided into two categories [3]. Classical approaches include **methods** such as time series models, regression models and techniques based on Kalman ﬁltering. Newer approaches apply **methods** from the research ﬁeld of artiﬁcial and **computational** **intelligence** such as artiﬁcial neural networks, fuzzy inference and fuzzy-neural models, expert systems, and support vector machines (SVMs). Although there is a large volume of literature on this topic, almost no applications of STLF to the thermal and electric loads of buildings have been reported [4]. Forrester and Wepfer [5], for instance, proposed a method based on multiple linear regression to provide forecasts of the energy demand of a large, commercial building. Dhar et al. [6] applied a Fourier series model to predict the hourly heating and cooling energy use in commercial buildings. Several researchers studied neural networks (NNs) to develop a building STLF algorithm [7, 8, 9]. Hou and Lian [10] studied in their work the feasibility and applicability of the SVM for the specific case of building **load** **forecasting**. In [4], the performances of an autoregressive model, an autoregressive integrated moving average (ARIMA) model, a NN and a Bayesian model for the **forecasting** of the electric **load** of an air-conditioned non- residential building are examined.

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Gaussian membership functions, it can approach a non-fuzzy set if the free parameter is tuned. Because of their smoothness and concise notation, Gaussian and bell membership functions are popular **methods** for specifying fuzzy sets. The curves have the advantage of being smooth and nonzero at all points. Season do not change frequently hence Gaussian membership function has been taken for the analysis [10].

error, regarding two different time horizons, is presented. Regarding the one step ahead, the results obtained, by the tested models, are all very similar to the one obtained by the autoregressive model. However the differences between models become more apparent when the **forecasting** horizon is increased. For the case of sixty steps ahead, the prediction error has a lower value for the hybrid SVR/Markov model when comparing to the remaining values. However the value achieved by this method is followed very closely by other models, in particular the SVR with simple Gaussian kernel (SVR-GK) and artificial neural network with wavelet decomposition (ANN-WD).

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We presented a refined parametric model for **short** **term** **load** **forecasting**. We began with a linear model, as in (1), that is simple and transparent, uses well-understood techniques and is easy and efficient to implement. A series of refinements improved the accuracy of our model; these included combining models from multiple weather stations, removing outliers from the historical data and special treatment of public holidays. The C++ code of our **forecasting** program is freely available under the GPL license and we encourage readers to experiment further with our **methods**.

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Due to various factors such as weather changes, social activities and festival types, the **load** appears as a non-stationary random process in time series, but most of the factors affecting system **load** have regularity, to achieve effective prediction. Foundation. In order to predict the **load** better, people use many **methods** to increase the accuracy of the **short**-**term** **load**. The newer algorithms mainly include the neural network method, time series method, regression analysis method, support vector machine method, and fuzzy prediction method[2]. The core problem of power **load** **forecasting** research is how to use existing historical data to establish a predictive model to predict the **load** value in future time or period. Therefore, the reliability of historical data information and prediction model are the central factors that affect **load** **forecasting** accuracy.

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This paper is designed to walk through our approach to this problem in four steps. First we will reveal the necessary background knowledge so that one becomes acquainted with the problem and how we are attempting to solve this problem. We will start with the history of artificial **intelligence**. We then will introduce the problem along with the necessary background knowledge to fully understand it. We then take a look at other approaches that have seen real world implementations. Next, we introduce the background knowledge of our proposed solution to the problem, beginning with neural networks. We then pull from recent academic literature on this subject to further build our approach. After all of this background analysis has been done we finally detail our approach which consists of threefold, an FFNN, LSTM, and a hybrid approach. Lastly, we detail and conclude our findings.

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A number of studies have developed different **methods** to accurately forecast electricity **load** in recent years. Traditional statistical **load** **forecasting** **methods** are inadequate to fully model the complex nature of electricity demand and often result in lower accuracy [2]. Artificial **Intelligence** (AI) based techniques are most favourable due to their ability to tackle non-linear relationships between dependent and independent variables. Fuzzy logic [3], artificial neural networks [4], sup- port vector machines [5] and wavelets neural networks [6] are popular AI techniques for STLF. Due to the state-of- the-art success of deep neural network **methods** in image processing, they are naturally being adapted for general time series modelling tasks such as those for **load** **forecasting**. Busseti et al. [7] predicted **load** demands by utilizing only time and temperature data by performing prediction **using** deep neural and recurrent neural networks. In fact, the outcomes behind the aforementioned study achieved an RMSE error of 530 KW/h **using** a three layer recurrent neural network. Khan et al. [8] proposed a recurrent neural network model for half an hour ahead **load** **forecasting** whereas Agarwal et al. [6] developed ANN models for hour ahead **load** and price forecasts **using** the **load** data from ISO New England 1 ) that

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A lot of researches were conducted based on AI **methods** [1], [3], [6]–[12], but there is room for improvements. As presented in [6] GA and PSO are used in training multi-layer perceptron NN (MLPNN) and compared with back propagation NN (BPNN). It was found that the GA trained NN is more accurate and slower in convergence than the PSO trained NN, but both are more accurate than the BPNN. A redial basis function (RBF) NN is proposed to forecast the **load** without considering the price factor [7], then the RBF-NN forecast is adjusted with real-time price **using** ANFIS. One-hour- ahead forecast **using** ANFIS is presented by Thai Nguyen and Yuan liao [8]. Next hour temperature, next hour dew point, day of the week, hour of the day and current day **load** are used as model inputs to the ANFIS model. M. Hanmandlu and B. K. Chauhan [9] presented a two hybrid NN models comprised of Fuzzy NN (FNN) and wavelet fuzzy NN (WFNN). Fuzzified wavelets inputs from WFNN are used in the FNN, which employed Choquet Integral through q-measure to simplify the learning process, and used reinforced learning to speed the

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Abstract: The **forecasting** of electricity demand has become one of the major research fields in Electrical Engineering. In recent years, much research has been carried out on the application of artificial **intelligence** techniques to the **Load**-**Forecasting** problem. Various Artificial **Intelligence** (AI) techniques used for **load** **forecasting** are Expert systems, Fuzzy, Genetic Algorithm, Artificial Neural Network (ANN). This research work is an attempt to apply hybrid and integrated effort to forecast **load**. Regression, Fuzzy and Neural along with Genetic Algorithm will empower the analysts to strongly forecast fairly accurate **load** demand on hourly base.

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important to select an appropriate hybrid model of ANN which gives less error in the STLF results. This includes inappropriate selection of back-propagation algorithm for ANN may cause to inaccurate estimation of STLF results. In this paper, the ANN is used to perform STLF for the next 24 hours. The performance of ANN is improved due to the significant input data provided by the sequential process of feature extractions. The first stage of feature extraction involves the transformation of raw data that is from the chronological hourly peak loads to the multiple time lags of hourly peak loads. The significant input data for ANN is finally obtained by **using** the principal component analysis (PCA) and this is considered as the second stage of feature extraction process. The final stage of improvement for the results of STLF is based on the stationary hourly peak loads of ANN output. The ANN can easily forecasts a stationary form of time series and this has been proven in (Mahdi et al., 2009), (Zhang, 2003), (Zhang et al., 1998), (Lachtermacher and Fuller, 1995), (Khotanzad, 1995), (El-Sharkawi, 1993). The performance of the proposed ANN in STLF is verified by **using** a case study of Malaysian hourly peak loads in the year 2002.

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The next step is to test the assumption of residual white noise. The results of this test showed that the models already meet the assumptions of residual white noise because p-value > 0.05. After that, we need to test the normality. the models generated from ARIMA method, although it has already been added outlier not all residual has normal distribution, This is because the data is in the leptokurtic form. Table 1 is a table of result model from ARIMA **methods** with added outlier by calculating MAPE, SMAPE, and RMSE of out sample’s data. The resulting models are Paiton 13.30 is (0,1,1) (0,1,1) 7 , Paiton 18.30 is (0,1,[1,2,3]) (0,1,1) 7 , Paiton 22.30 is (0,1,[1,8]) (0,1,1) 7 .

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existing regression model **using** the same data. The NN-based model produced more accurate results in terms of forecast errors, and was robust, adaptive to weather changing conditions. According to Hong and Fan (2016), one of the most successful implementations of NN models for STLF was developed by Khotanzad et al. (1997) and sponsored by the Electric Power Research Institute (EPRI). The authors nevertheless admitted that a couple of conventional techniques were used previously with varying degrees of satisfaction, but not as accurate as would be desired. Besides, most of these models could not be used elsewhere, but at the built site. This paper investigated several types of NN architecture such as recurrent NN and radial basis NN, and came to a conclusion that there was no major advantage of these architectures over the MLP in terms of the **load** **forecasting** problem. This NN **Short**-**Term** **Load** Forecaster (ANNSTLF) constructed by the aforementioned authors, was subjected to different comparative studies **using** various **methods** as well as other NN-based models. The accuracy of the **load** forecasts was evaluated and expressed in terms of the MAPE. The ANNSTLF yielded very good results and induced its acceptance across Canada and USA.

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Broadly, the **load** **forecasting** techniques can be divided into two categories such as parametric or non parametric techniques. The linear regression, auto regressive moving average (ARMA), general exponential technique and stochastic time series techniques are some examples of parametric (statistical) technique. The main drawback of this technique is its capability in abrupt change of any types of environment or social changes. However, this shortcoming is overcome by applying non- parametric (artificial **intelligence**) based technique because of its potentiality to global search. Among these artificial **intelligence** based methodology, artificial neural network has emerged as one of the most prominent technique that receive much more attention of researchers. The ability to solve the complex relationships, adaptive control, image denoising, decision making under uncertainty and prediction patterns makes ANN a powerful performer than previously implemented techniques [7-11]. Hence, several variants of ANN which is generally hybridization of neural network with some learning techniques such as GA, PS0, BFO etc are proposed by several researchers. Similarly, ANN in hybridization with fuzzy logic, AIS (Artificial Immune System), LMA (Levenberg Marquardt Algorithm) etc have also shown improved performance in terms of accuracy, **computational** cost and time requirement. In sec 2, a brief discussion on ANN has been done. In section 3, different variants of ANN, that includes the conventional and hybrid neural network techniques that have successfully applied to STLF is described. Finally, the conclusion of the present review work has been presented in section 4.

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IJEDR1504164 International Journal of Engineering Development and Research (www.ijedr.org) 938 Artificial **intelligence** tools **using** to improve the **forecasting** result. It is very difficult to predict the **load** accurately **using** analytical **methods**. A neural network is the advanced approach for accurate **short**-team **load** **forecasting**. **Short**-team **load** **forecasting** is **using** Artificial **intelligence** method and error minimized.

IJEDR1602012 International Journal of Engineering Development and Research (www.ijedr.org) 79 **Load** **forecasting** is an essential tool for operation and planning of power system. It is required for unit commitment, energy transfer scheduling and **load** dispatch. The different types of **load** **forecasting** [5] can be classified according to forecast period as: a. **Short** –**term** **load** **forecasting** (STLF), which are usually from one hour to one month. It is important for various applications such as unit commitment, economic dispatch, energy transfer scheduling and real time control. A lot of studies have been done for **using** of **short**-**term** **load** **forecasting** [5] with different **methods**. Some of these **methods** may be classified as follow: Regression, Kalman filtering, Box &Jenkins model, Expert system, Fuzzy inference, Neuro-fuzzy models and Chaos time series analysis. Some of these **methods** have main limitations such as neglecting of some **forecasting** attribute condition, difficulty to find functional relationship between all attribute variable and instantaneous **load** demand, difficulty to upgrade the set of the rules that govern at expert system and disability to adjust themselves with rapid nonlinear system –**load** change. The NNs can be used to solve these problems. Most of these projects **using** NNs considered many factors such as weather condition, holidays, weekends and special sport matches days in **forecasting** model, successfully. This is because of learning ability of NNs with many input factors.

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