1. LOAD FORECASTING USING ANN UNDER THE GUIDANCE OF PROF. G.N.GOYAL NEHA CHOURE (04) AVI AGRAWAL (110) SUMIT PARKHADE (118) HITESH CHAUDHARI (126) DEPARTMENT OF ELECTRICAL ENGINEERING SHRI RAMDEOBABA COLLEGE OF ENGINEERING & MANAGEMENT

2. WHAT IS LOAD FORECASTING? Electric load forecasting is the process used to forecast future electric load, given historical load, weather information along with current and forecasted weather information. Load forecasting plays a key role in helping an electric utility to make important decisions on power, load switching, voltage control, network reconfiguration, infrastructure development, purchasing and generating electric power.

3. Load forecasting is a technique used by power or energy- providing companies to predict the power/energy needed to meet the demand and supply equilibrium. The accuracy of forecasting is of great significance for the operational and managerial loading of a utility company.

4. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. However, with the deregulation of the energy industries, load forecasting is even more important. Load forecasting can be divided into three categories. Short term load forecasting which from are usually one hour to one week period. Short term load forecasting can help to estimate load flows and to make decisions that can prevent overloading. Medium load forecasting which are usually from a week to one year, and Long term load forecasting which are longer than a year Approximately nine categories of load forecasting techniques has been reported in the literature

5. WHY LOAD FORECASTING Proper planning of power system is a very important part of power systems operation. Load forecasts are an important factor for planning in the long, short and medium term. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company.

6. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Load forecasts are extremely important for energy suppliers, ISOs, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets

7. METHODS OF FORECASTING Statistical model-based learning Artificial Neural Network(ANN) Method Expert systems Method Fuzzy logic Method Regression methods. Time series

8. WHAT IS ANN? A neural network is a machine that is designed to model the way in which the brain performs a particular task. The network is implemented by using electronic components or is simulated in software on a digital computer. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.

9. A neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use. There are two different types of layers: 1.Single layer ANN 2.Multi-layer ANN

10. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times. The original goal of the neural network approach was to solve problems in the same way that a human brain would.

11. WHY DO WE USE NEURAL NETWORKS Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Benefits: 1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.

12. 2. Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time. 3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.

13. ANN LOAD FORECASTING MODEL The developed forecasting model, which considers temperature and humidity as input data, is shown in Fig. 1. Fig.1 The proposed ANN Model

14. Since the load demand does not depend only on temperature and humidity and in order to account for other factors, the input data are fed to the neural network with historical load data for training and comparing for future load forecasting. Neural network elements might be arranged in different number of layers between the input and output; however, in practice, the number of used layers is relatively small.

15. TYPES OF ANN Feed forward Network Recurrent Neural Network Regulatory feedback Network Radial Basis Function

16. ADVANTAGES A neural network can perform tasks in which a linear program cannot perform. When an element of the neural network fails, it can continue without any problem by their parallel nature. A neural network does not need to be reprogrammed as it learns itself. It can be implemented in an easy way without any problem.

17. As adaptive, intelligent systems, neural networks are robust and excel at solving complex problems. It can be implemented in any applications without any risk. It generalizes knowledge to produce adequate responses to unknown situations.

18. APPLICATION Artificial neural network applications have been used in the field of solar energy for modelling and design of a solar steam generating plant. They are useful in system modelling, such as in implementing complex mapping and system identification. ANN are used for the estimation of heating-loads of buildings, parabolic-trough collector’s intercept factor and local concentration ratio.

19. ANN are used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization and signal processing. They are able to handle noisy and incomplete data and also able to deal with non-linear problems. The use of ANN in ventilating, air conditioning, heating, Load Forecasting and control of power generation system.

20. DATA COLLECTED All data are collected from Mankapur substation of 4 feeders(Ambajhari-1 & 2,Hingna-1, Uppalwadi) for the months of June, July and August. The following parameters are- Maximum Voltage Minimum Voltage Maximum Current Minimum Current MWH Meter Reading MWH consumption Temperature

21. ELECTRICAL PARAMETERS FOR THE MONTH OF JUNE MONTH:JUNE S.No.DateTime 132kV Ambajhari-I132kV Ambajhari-II VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter Reading MWH MaxMinMaxMinMaxMinMaxMin 1 01-06-201708:00135.4138.73372074144882 135.4138.72721671934669 2 02-06-201708:00133.4138.335521641461941312133.4138.328617419357701101 3 03-06-201708:00133.5137.937222741474311237133.1137.529918319368841114 4 04-06-201708:00134.2137.735823341487861355134.2138.728818819379971113 5 05-06-201708:00135.4137.733924141500981312134.8137.727719419391521155 6 06-06-201708:00133.7138.736022041515631465133.3138.729017719403261174 7 07-06-201708:00135.7139.132419741528871324135.7139.126115919415981272 8 08-06-201708:00132.314033420341541911304132.314026716319427901192 9 09-06-201708:00135.8138.932821241556821491135.3138.426317019440241234 10 10-06-201708:00136.2139.631920741571771495135.3139.624616619452601236 11 11-06-201708:00136.9139.829819741585301353136.9139.823915819463781118 12 12-06-201708:00137.2139.229319441598241294136.7139.523415619474471069 13 13-06-201708:00135.214030919541611361312135.2139.624615619485331086 14 14-06-201708:00136.3140.631818041624271291136.3140.625614519496311098 15 15-06-201708:00134.7139.531720541637581331136.6139.124116519507071076 16 16-06-201708:00135.6140.730919941651081350135.2140.124716119518261119 17 17-06-201708:00135.4139.731419841663551247135.4139.725215919528591033 18 18-06-201708:00135.1138.733221041678351480135.1138.726716919540851226 19 19-06-201708:00134137.634621541692881453133.6137.227817219552891204 20 20-06-201708:00133.7137.334121741708021514133.3136.927317419565441255 21 21-06-201708:00133.5138.142118341722871485135.9138.124714819577751231 22 22-06-201708:00134.2138.930718241738941607134.2138.92461471958402627 23 23-06-201708:00134.5139.237918441752211327135.7138.822814819595011099 24 24-06-201708:00135.1138.640424841768131592 NA 1959772271 25 25-06-201708:00135.4138.439625641786161803136.713723220919597720 26 26-06-201708:00137.313928019641800691453137.31392251571960358586 27 27-06-201708:00139.3140.724818341812311162137.9140.72031471961322964 28 28-06-201708:00139.8141.623017341823741143139.8141.61851391962270948 29 29-06-201708:00137.614026812141834381064137.21402161381963153883 30 30-06-201708:00137.9139.126817241845761138137.6139.12151391964097944 31

22. 132kV Hingna-I132kV Uppalwadi VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter ReadingMWH MaxMinMaxMinMaxMinMaxMin 135.7136.192622950778 135.7136.13062166490778 135.5136.292592951034256134.6138.331419064919651187 133.5136.399632951370336133.1137.634921364930071042 134.2138.7101742951625255134.2138.73362256493986979 134.8134.298532951878253135.8134.632314664950971111 135.5136.895592952173295134138.73692076495885788 135.9137.195592952498325135.8137.13851886496772887 136.4136.391592952722224135.714038023464979611189 135.7136.989592952966244135.6138.53702416498740779 136.2137.990442953189223136.2137.937222565002551515 136.9138.788592953376187136.4139.835022165016501395 137.2136.389522953601225137139.234521465030291379 137.1138.387462953793192136.9138.233517065043481319 136.314093602953985192136.3140.633519065055981250 136.6138.594622954270285136.4139.132821765068591261 137.7139.589602954529259137.5140.433523465082171358 135.4138.199622954753224135.4139.735823265094801263 135138102712955052299135.1138.736124365109891509 135.2136.599622955386334133.6137.237023165125081519 133.9136.7102592955665279133.813736822565139621454 133.5138.1135552955883218135.4137.232911665152971335 136.313783502956269386136.3138.930017865163871090 136.2139127532956433164136.4138.928917965175811194 135.1138.4132852956798365135.4138.42431506518359778 135.2137.2140692957263465136.8138.13131576519253894 137.3139.282482957594331137.8138.730621665203151062 138.6139.278492957784190138.3140.728319565214431128 139.8138.676572957979195139.814026017765225101067 139.5138.473532958188209137.2140.12891806523441931 138.5138.973532958375187137.7138.728017965244581017

25. ELECTRICAL PARAMETERS FOR THE MONTH OF JULY MONTH:JULY S.No.DateTime 132kV Ambajhari-I132kV Ambajhari-II VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter Reading MWH MaxMinMaxMinMaxMinMaxMin 1 01-07-201708:00137.6140.734417241857511175138.4140.31801391965071974 2 02-07-201708:00139.4141.232322641872101459NA 1965227156 3 03-07-201708:00137.8140.133724141887041494NA 19652270 4 04-07-201708:0013714032724941902921588NA 19652270 5 05-07-201708:00138.5139.235223841918351543NA 19652270 6 06-07-201708:00135.2138.938126041934211586NA 19652270 7 07-07-201708:00134.7137.740625941951521731NA 19652270 8 08-07-201708:00133.6137.242527741969791827NA 19652270 9 09-07-201708:00135.3136.639729141988671888NA 19652270 10 10-07-201708:00134.9135.838430642006471780NA 19652270 11 11-07-201708:00136.2137.235725442023061659NA 19652270 12 12-07-201708:00138.3138.234224442039271621NA 19652270 13 13-07-201708:00138.2139.434527242054901563NA 19652270 14 14-07-201708:00138.514035327142072001710NA 19652270 15 15-07-201708:00137.5140.135826642088951695NA 19652270 16 16-07-201708:00139.3139.933026242105271632NA 19652270 17 17-07-201708:00138.1139.434827042121281601NA 19652270 18 18-07-201708:00137.3140.535723542137181590NA 19652270 19 19-07-201708:00138.2141.232821742152431525NA 19652270 20 20-07-201708:00137.3140.834925942167601517NA 19652270 21 21-07-201708:00136.814035825042183471587NA 19652270 22 22-07-201708:00137.314036326242199901643NA 19652270 23 23-07-201708:00138.1140.334325442216471657NA 19652270 24 24-07-201708:00137.814034525842232881641NA 19652270 25 25-07-201708:00137.8139.535127242249391651NA 19652270 26 26-07-201708:00139.514031125242265971658NA 19652270 27 27-07-201708:00138.2139.230924842280521455NA 19652270 28 28-07-201708:00137.9140.934924742295361484NA 19652270 29 29-07-201708:00136.2138.434723942311831647136.9137.922519119652270 30 30-07-201708:00136.9139.827320742325981415136.9139.22191751965949722 31 31-07-201708:00135.7138.629121842338831285135.3138.223417519670141065

26. 132kV Hingna-I132kV Uppalwadi VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter ReadingMWH MaxMinMaxMinMaxMinMaxMin 137.3138.1121512958578203139.2138.623617665255311073 139.2141.2119902958979401139.3141.22161566526375844 139.3139.9118862959449470138.7139.72141566527251876 141.5127.9117872959891442141.3139.72161396528070819 138.4138.6138872960336445138.1138.52431476528904834 135.9137.7139962960843507136.3138.92701816529785881 134.7137.7149942961361518134.3137.32801506530781996 133.3136.21881022961918557133.1136.930519465318361055 134.7136.41501062962551633133.5136.527121265329831147 134.9136.5151882963158607134.9135.627418165341191136 136.8136.7130922963658500136.2136.723716465353041185 138138.4132942964159501137.9138.72461646535954650 138.5139.41371062964689530138.7139.42622076536908954 138.8138.4127942965230541138.7138.225819665380271119 138.2138125872965748518139139.825118465390911064 139.3139.21331002966235487139.3139.525820065401011010 139.5138.6129992966756521139.413924518865411441043 136.9141.1123852967231475136.8140.53351446542098954 137.9140.7140852967684453140.2140.91991386542910812 140.7139.11381012968238554137.3140.22271566543675765 136.5140.2126922968739501136.3140.82431726544543868 137.31401281002969240501137.314026019365455431000 137.81401251022969756516139.1140.325420965466361093 138.7138.91301002970292536137.8139.629020165477881152 136.8139.1124982970823531139.7138.425621665488841096 139.5139.8126902971348525139.4139.725215965499671083 140.4138.9116872971824476140.6138.92261756550860893 137.4140.91251042972271447140140.52592036551754894 140.3138.112165297279552413714035222565528641110 137.5139.290652973154359136.9139.834528465542871423 137.2134.191692973466312136.3138.335028565558791592

29. ELECTRICAL PARAMETERS FOR THE MONTH OF AUGUST MONTH:AUGUST S.No.DateTime 132kV Ambajhari-I132kV Ambajhari-II VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter Reading MWH MaxMinMaxMinMaxMinMaxMin 11.8.1708:00134.613730922642352671384134.2136.624718119681611147 22.8.1708:00134.9137.739619942366771410136.6137.223716019693301169 33.8.1708:00136.3136.932622042380321355135.9136.526217619704541124 44.8.1708:00136.513631425942395371505132.4136.226220919717031249 55.8.1708:00136137.42802184240527990135.6137.922417419728541151 66.8.1708:00136.913729422642422551728136.5 23517419739551101 77.8.1708:00137.4139.224820642435711316137.4136.920016619750451090 88.8.1708:00138.6139.625018642447271156138.3139.21991491976004959 99.8.1708:00139138.722516842458221095138.6138.31811341976911907 1010.8.1708:00137.7139.52711724246815993137.7139.52171381977734823 1111.8.1708:00137.513927418642479971182137.5138.32201491978716982 1212.8.1708:00137.1138.728619142492471250136.7138.222915419797521036 1313.8.1708:00137138.427420042505441297137.5138.421816119808271075 1414.8.1708:00137.5137.327521842518151271137.5137.322117519818821055 1515.8.1708:00137.7140.225019142530951280137.2139.32011531982840958 1616.8.1708:00135.7139.129019142542311136135.1138.72351531983783943 1717.8.1708:00136.9137.928320142555481317136.4137.422816119848781095 1818.8.1708:00136139.127220142568491301136139.121816119859571079 1919.8.1708:00138.3140.626117742580521203138.3140.62081771986955998 2020.8.1708:00139.2140.626318642592991247138.7140.221115319879911036 2121.8.1708:00140.3141.424019042605061207140.3141.41921521988990999 2222.8.1708:00140.4142.222917442616241118140.4142.11841401989917927 2323.8.1708:00138.7141.527918242627011077138.31412241401990812895 2424.8.1708:00138.2140.327520542639001199138.2140.32211641991807995 2525.8.1708:00139.5141.127019342651571257139.1141.121615519928491042 2626.8.1708:00137.6140.832319842664131256138.2140.323415919938901041 2727.8.1708:00139.9140.335420542678321419139.8140.320316519950661176 2828.8.1708:00138140.727420242690701238137.7140.822017119960921026 2929.8.1708:00139.6141.826218742703141244139.6141.320916319971231031 3030.8.1708:00140142.324917442714681154140141.72001401998080957 3131.8.1708:00138.9141.928219242725981130138.3141.52261541999018938

30. 132kV Hingna-I132kV Uppalwadi VoltageCurrent MWH Meter Reading MWH VoltageCurrent MWH Meter ReadingMWH MaxMinMaxMinMaxMinMaxMin 135.7136.597692973792326135.6136.637129565575161637 134.9136.198662974124332135.2137.338523665591881672 136136.7115692974490366135.2136.935828065607941606 136.5136115662974881391136135.638830865626991905 134.1136.88760297519131013313736628465641151416 136.7136.893622975494303135.7137.539030565659961881 137.4139.288482975845351137137.834123065677111715 138.713877402976075230138.5137.728618565689641253 138.8138.676492976271196138.7138.625716465700871123 137.7138.777502976510239137.7139.33181876571078991 139.113980552976760250137.1138.432421965723641286 137.5137.790562977027267136.1138.335423065737701406 136.9137.1102692977347320136.713836225865753271557 136.6135.995672977696349137.6137.334227765768671540 13914097762977993297137.2139.433723965782441377 136.9138.9107762978374381136.8135.335024665796161372 138.1137.998752978708334134.1137.634724765810331417 137.7137.595682979078370135.7139.231722665825141481 139.1139.976582979387309138.3140.628818765837741260 140.2140.477572979652265138.714031021765851251351 140.3140.478532979931279140.8139.827820665865121387 140.3141.681542980185254140.3141.427219265876821170 138.4139.394542980471286138.3139.234519365888551173 139.8138.988602980779308139.2140.332324965901811326 139.3140.182652981051272139141.133624965916101429 138140.6103642981355304138.4140.436024165931241514 140.5139.380642981717362139.2140.331227365949581834 140.4138.978572982017300138.9140.531023565964271469 141.7141.373462982267250139.6141.930119465977861359 140.71417443298244818114014127015265989951209 138.6138.878502982750302138.4139.131620166001551160

33. LOAD FORECASTING USING ANN Short-term power load forecasting is used to provide utility company management with future information about electric load demand in order to assist them in running more economical and reliable day-to-day operations. A broad spectrum of factors affect the system’s load level such as trend effects, cyclic-time effects, and weather effects, random effects like human activities, load management and thunderstorms.

34. Thus the load profile is dynamic in nature with temporal, seasonal and annual variations. In our project we developed a system that predicted 24 hour at a time load demand. As inputs we took the past 24 load and the day of the week. Input to the ANN are past loads and the output of the ANN is the load forecast for a next day.

35. The inputs were fed into our Artificial Neural Network (ANN) and after sufficient training were used to predict the load demand for the next week. A schematic model of our system is shown in Fig 5.1. The inputs given are: 1.Hourly load demand for the full day. 2.Day of the week. 3.Min/Max/ Average daily temperature. The output obtained was the predicted hourly load demand for the next day.

36. The flow chart is shown below

37. The ANN was implemented using MATLAB 15. The training algorithm was used which is an adaptive learning algorithm using the epoch method of training. The number of epochs while training was set at 50,000 by which point the network was sufficiently trained.

38. IMPLEMENTATION Gathering and arranging the data in MS Excel spreadsheet. Tagging the data into groups. Analyse the data. MATLAB simulation of data using ANN.

39. CONCLUSION The historical data of 3 months collected from Mankapur substation and physical parameters like temperature and humidity are used for analysis and this data is used in MATLAB simulation using ANN for further analysis. FUTURE WORK Using this data and MATLAB simulation using ANN, we can analyse the electrical parameters of next month and this can be used in generating station for required generation of power and the effect of temperature and pressure can be determined.

40. REFERENCES Mohan B. Tasre,Prashant P. Bedekar and Vilas N. Ghate,"Daily Peak Load Forecasting Using ANN",IEEE Trans.,8-10 December 2011. Mohammad Ghomi, Mahdi Goodarzi, Mahmood Goodarzi, “Peak Load Forecasting of Electric Utilities for West province of Iran by using Neural Network without weather Information”,IEEE Trans.,12th International Conference on Computer Modelling and Simulation 2010,Oct 2010.

41. THANK YOU