IIIT Hyderabad Publications |
|||||||||
|
Short Term Load Forecasting for Smart Power SystemsAuthor: Babita Jain Date: 2018-11-28 Report no: IIIT/TH/2018/80 Advisor:Amit Jain AbstractShort Term Load Forecasting (STLF) has always been one of the most critical, sensitive and accuracy demanding factors of the Power Systems. An accurate STLF improves not only the system’s economic viability but also its safety, stability and reliability in operation to realize futuristic Smart Power System. The research presented in this work supports the argument of hybrid approach whereby the complementary strengths of different intelligent techniques are combined to offer better solution to the problem of STLF. This work presents Artificial Intelligence (AI) and Data Mining based formulation for STLF combined with Statistical Techniques. The Temperature, Humidity and Day Type are considered as they are significant factors impacting the effectiveness of an accurate STLF. A Euclidean Norm based Similar Day Approach using the Correction Factors generated by the Fuzzy Inference System has been developed initially for the STLF and novelty is introduced in this methodology by assigning weights to various variables used in the Euclidean Norm to increase the effectiveness of various variables in deciding the similarity. The Fuzzy Inference System (FIS) is designed with three input membership functions, Load Difference (ΔE L ), Temperature Difference (ΔE T ) and Humidity Difference (ΔE H ) taking Trapezoidal Memberships and one output membership function which is the Correction Factor taking a Triangular Membership. Mamdani inference system is used for the FIS. Twenty seven rules are predefined based on the data history and these rules govern the functioning of the FIS. This Euclidean Norm based FIS is used to perform the STLF. The methodology basically uses the Euclidean Norm to select the few similar days of the forecast day from the historic dataset. These similar days of the forecast day are then corrected using the correction factors generated by the FIS that uses the forecast previous day and its similar days’ hourly load, hourly temperature and hourly humidity differences as the input to the FIS. The average of the corrected hourly loads of the similar days of the forecast day is then considered as the hourly load of the forecast day. The Euclidean Norm based FIS has been tested on a dataset of 7 months data. Further to this the input parameter limits of the FIS have been optimized using three Swarm Intelligence Techniques namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The optimized PSO-FIS, NPSO-FIS and EPSO-FIS were used to perform the STLF on a 7 months dataset as well as data of 3 years as historic dataset. The results of all the three techniques were found to be good. Further another novel methodology, which amalgamates the Clustering Technique of Data Mining with the Regression Technique, has been developed in this thesis to give a more accurate STLF. The framework of the Clustering based Regression methodology is detailed as follows: to forecast the load of a given day, the forecast day and the similar days (all Mondays for Monday and likewise) of the forecast day selected from the previous two years history data are clustered using the k- medoids clustering algorithm, based on their similarity of weather variables i.e. Temperature and Humidity. Further, the proposed method finds the Regression constants from the cluster matrix containing the forecast day. The load forecasting for the forecast day is then carried out using the Regression Equation. The zest of this technique is that clustering brings together the very similar days of the forecast day in one cluster and regression technique further encapsulates the total correlation of load and weather variables of the similar days in the cluster, hence enhancing the forecast efficiency. Historical data set, which captures the impact of weather variables on the load, is used for simulation studies and the proposed technique has been found very successful for load forecast of all days and all seasons. The research presented in this thesis also deals with the very important issue of STLF for special days. This research presents a Hybrid Data Mining based formulation of STLF with emphasis on special days and anomalous days, such as public and national holidays, which are often ignored during the general modeling process. The methodology proposed in present research generates STLF for special days using an Artificial Intelligence based formulation combined with Statistical Techniques. The methodology basically uses the Clustering technique of data mining and combines it with the Regression technique with a variation in the selection of days in historic data set. The proposed framework is detailed as follows: to forecast the load of a special day, the similar days (all earlier special days and the weekends) of the special day selected from the same months of previous two years in historic data are clustered using the k-medoids clustering algorithm based on their similarity of weather variables i.e Temperature and Humidity. The proposed method finds the Regression constants from the cluster matrix containing the special day. The load forecasting for the special day is then carried out using the regression equation. The proposed technique has been very successful for load forecasting of all special days of all seasons. The STLF has been carried out using the technique for a real time data set of three years with a history data set of two years for each year and the results of all special days of all the three years have been found to be very satisfactory. A comparative analysis of results has been done for the proposed technique with the Standard Regression Technique, Weighted Euclidean Norm based Similar Day with Fuzzy Logic Technique and with the Weighted Euclidean Norm based Similar Day New Particle Swarm Optimization (NPSO) technique further optimized by Fuzzy technique. The proposed technique shows improved performance in comparison to the others. The results have been quite encouraging with the Mean Absolute Percentage Error (MAPE) for most of the special days coming out to be less than 3.0%. A comparative result analysis has been carried out for all the developed techniques for a 7 months dataset and also a 3 years dataset for normal days and 3 years dataset for special days. The results have been quite encouraging for all proposed techniques though the proposed Clustering based Regression Technique showed an edge in performance in comparison to the other proposed techniques in some cases. For performance evaluation, the results of the developed techniques have been compared with the standard regression line method for the same dataset. The research work in this thesis presents simple, holistic and efficient techniques for accurate STLF not only for normal days but also for the special days and also validates that these techniques generate better results in comparison to the Traditional Regression Technique and other Swarm Intelligence based Fuzzy Techniques developed by the researchers for all days of all seasons and also for the special days. Full thesis: pdf Centre for Power Systems |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |