Short Term Load Forcasting using Heuristic Algorithm

Short Term Load Forcasting using Heuristic Algorithm and Support Vector Machine Orooj Nazeer1 , Nadeem Javaid2,∗ , Abdul Basit Majeed Khan1 , Arif Hussain3 , Tariq Basheer4 , Muhammad Mukhtar Ahmed Ratyal5 1

Abasyn University Department of Computing and Technology, Islamabad 44000, Pakistan 2 COMSATS Institute of Information Technology, Islamabad 44000, Pakistan 3 Islamic International University, Islamabad 44000, Pakistan 4 The University of Lahore, Gujrat Campus 10240, Pakistan 5 Mirpur University of Science and Technology, 10250, Azad Kashmir ∗ Correspondence: [email protected], www.njavaid.com

Abstract. Analysis of data is very important for accurate prediction. Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is used for load forcasting. Features are selected using PSO and redundant features are removed. Data is divided into training and testing data. Load forecasting is done by using SVM classifier. However, SVM classifier predicts short term load accurately and efficiently. Multiple time testing is done on data for checking accuracy of PSO. SVM shows efficient performance as compared to Principle Component Analysis (PCA).

1 Introduction For economic development of a country load forcasting is playing very important rule. Efficient usage of energy consumption in short term load forcasting is influential. Load forcasting plays an important rule to create a balance between supply and demand side. Many methods are used for load prediction. However, when load is predicted in inaccurate way than there should be chances of loss. To fulfills the growing amount of power needs absolute forcasting of power load is required. Prediction of load can be long term or short term. Utility is used in a system and how it fulfill the demand of load however, big data problems are addressed in [1]. Complexity and volume of data is increasing day by day. To addressed this challenge, big data technology is used in [2]. Smart grid consists of smart meters and sensors. Scheduling of energy is done by using hybrid approach. Main aim of short term load forcasting is predicted value of load peaks during the day. Decision making is done using these prediction values. Scheduling of security, planning, purchasing is done on the basis of forcast value. Forcasting plays important rule for a bright future. Many technologies like filtering, state space are used for load forcasting. Huge amount of memory, an effective duration of time is required for processing of big data. Many optimization techniques are used for feature selection

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however, convergence of existing techniques more in local optima. PSO performs well as compared to existing techniques. Main objective of PSO is reduction of features for improving performance accuracy. The remaining paper is organized as follows: section II contains the related work. System model is presented in section III. Computations and analysis are discussed in section IV. Finally paper is concluded in section V.

2 Related work Authors in [3], proposed a Modified Particle Swarm Optimization (MPSO) for selection of features. Particle fitness is calculated using Support Vector Machine (SVM). MPSO shows effective results as compared to existing techniques. A hybrid is proposed in paper [4], for classification of cancer. For classification of cancer a universal technique is used. Proposed approach shows more reliability and efficiency in classification as compared to existing techniques. Features are selected in paper [5], using a novel hybrid algorithm which is based on PSO. Features are selected on the bases of correlation among themselves. Hybrid PSO with Local Search Operations (LSO) is used for knowledge relation in local space. In [6, 7], regression technique is used for estimation of parameters in semisupervised learning. Accuracy of process is increased by using SVM. Proposed regression technique shows an optimal solution on training data which represent efficiency of technique. A hybrid of SVM and Self Organised Map (SOM) is used for forcasting short term load. Electricity load of next day predicted using this hybrid method. For financial load forcasting SVM is used in [8]. Forcasted results of SVM is compared with Back Propagation (BP) and Radial Basis Function (RBF). SVM shows effective results as compared to others techniques. Efficient energy management system is required with icreasing number of population. Therefore, energy forcasting is necessary for proper management. Authors used Artificial Intelligence (AI) techniques in [9] to forcast load. AI techniques are compared with existing techniques however AI techniques shows optimal results in forcasting load as compared to others techniques. For efficient learning a novel scheme is used in [10] for the purpose of scheduling. Features are selected and classification is performed using linear regression. Proposed method is compared with other methods using classifiers. However, proposed optimizer is more scalable in computations as compared to others methods. Data is increasing daya by day. With this increased amount of data some processes are required to tackel this huge amount of data on demand and supply side. Authors in [11], discussed about data security and privacy, proposed some techniques to handle huge amount of data. Authors in [12], performs real time analysis on different type of data. Resources of data are checked by using proposed data driven technique. Data correlation is found by using Random Matrix Theory (RMT). IEEE 118-bus system is used as a classifier is used for the purpose of validation. Genetic Algorithm (GA) is used for prediction of load in [13]. Parameters of GA is optimized for improving the accuracy. GA performs accurately as com-

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pared to others techniques. Two years data is considered as training data and 9 years data is considered as testing data. Authors in [14, 15], used a non linear method for forcasting price of carbon. Decomposition of data is done using SVM classifier. Open source data is used for forcasting load while usig a weight appoarch.

Fig. 1. System model

3 System model In this section, architecture of our proposed system is described in detail, as shown in Fig. 1. System model consists of transmission lines, end users and power generation plants. Big data which consists of huge amount of data. End users may be from industry, commercial or residential users. Smart Meters (SM) collects data from smart homes and sent towards storage device. Features are selected using PSO, short term forcasting of load is done using SVM. 3.1 PSO for feature selection Number of features can be reduced using feature selection methods. However, redundant and less informative features are eliminated. In past, PSO is only used for solving non-linear optimization problems. In this way, computation time and accuracy of process is improved. However, PSO is recently used to tackle real

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Fig. 2. Flow chart

life application issues. PSO is based on population which contains particles and group of swarm. Particles are moving with specific velocity for finding result in search space. Step by step process of algorithm is given as follows: – Particles, velocity and positions are initialized. – Fitness of every particle is calculated.

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Global and local best are updated. Position is updated. Velocity is updated. If best solution is met stop else go to step 2. Flow chart of PSO is shown in Fig. 2.

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Fig. 5. Error analysis

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Fig. 6. Day load

4 Computation and analysis 4.1 SVM based on PSO Particle Swarm Optimization (PSO) is used for feature selection and load is forcasted by Support Vector Machine (SVM). Step 1 Rather than historical load data, daily lowest, highest and average temperature also selected. Using PSO, data is divided into training and testing data on the basis of features similarity. Step 2 SVM model is used for forcasting, data which is optimized using PSO optimization technique. Some features like highest temperature, average tem-

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Fig. 7. Week load

perature and lowest temperature are also considered. Firstly initialized SVM parameters and random values are assigned. 4.2 Selected sample Power load data is taken from Inner Mongolia region May,2004 to March,2006 data is considered as training data and March,2006 to May,2006 testing data. 100 cities power supply is covered by this region. There occurs changing pattern of load forcasting. Inner Mongolia region satisfied power load demand of own region and also supply power in North China. For improving reliability and security of electricity load, Inner Mongolia region is paying more attention on load forcasting. It is observed that during different period of time load is different. 4.3 Analysis of error Relative error is used as a evaluator at specific time between the actual load and forcasting load. A(t) is actual load and F(t) is a forcasted load. 4.4 Reduction of attribute using PSO performance of algorithm is assesssed in this section. Windows XP is used as a operating system. Parameters of experiments are confirmed by considering large numbers of experiments. Fig.3, shows evolution curve of sample data. Total number of attributes are shown by attribute no. After processing reduced attributes are shown by reduced no. Redundant samples are removed and optimal results are saved in solution. Selected attributes are 5 from the 10 number of attributes.

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4.5 Forcasting through SVM SVM is used for forcasting the load. MATLAB is used for computation of results. SVM model take only 14 s for training data. Result of forcasting load and actual load are shown in fig.4. Minimum and maximum deviation od forcasting load and actual load are calculated. In actual load maximum deviation is -30.57 MW while using SVM maximum deviation is 23.93 MW. SVM model accurately forcast the load. A comparison is made between actual value and forcast value. SVM model forcast load efficiently. 24 hours load prediction curve through SVM and actual load is shown in fig.5. SVM model has precision in error analysis as compared to PSO. Forcasting error of SVM model is lower than PSO. 4.6 Power load data feature Various factors of short load forcasting are discussed in this section. Firstly, proper input variables are chosen, which is an important challenge. Load curve analysis is used for variables selection. Load is regularly changing from one hour to next hour. Load data of a day is shown in fig.6. Peak of load occurred doubly be about 9:30 AM and 17:00 PM in a day. Forcasted load of a week is shown in fig.7, peaks are high on first to third days of a week. It shows that more power consumption on starting days of a week.

5 Conclusion Deal with big data while using traditional approaches is difficult. Big data is considered as for computation and analysis. Forcasting of price plays an important rule for efficient for managing demand and supply side. Many methods are used for forcasting cost. SVM is used for forcasting the load. PSO is used for selecting features. PSO removes redundant and useless information. 70% data is used as training and 30% is used as testing. Comparisons shows that SVM performs very well as compared to PCA for forcasting the load.

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