Load forecasting plays an important role in power system planning, operation and control. Forecasting means estimating active load at various load buses ahead of actual load occurrence. Planning and operational applications of load forecasting requires a certain ‘lead time’ also called forecasting intervals. Nature of forecasts, lead times and applications are summarized in Table 1.
A good forecast reflecting current and future trends, tempered with good judgement, is the key to all planning, indeed to financial success. The accuracy of a forecast is crucial to any electric utility, since it determines the timing and characteristics of major system additions. A forecast that is too low can result in low revenue from sales to neighbouring utilities or even in load curtailment. Forecast that are too high can result in severe financial problems due to excessive investment in a plant that is not fully utilized or operated at low capacity factors. No forecast obtained from analytical procedures can be strictly relied upon the judgement of the forecaster, which plays a crucial role in arriving at an acceptable forecast. Choosing a forecasting technique for use in establishing future load requirements is a nontrivial task in itself. Depending on nature or load variations, one particular method may be superior to another.
Here one of the author of this article has shared some useful information in the matter as a practicing engineer having long experience of 26 years working at power system operation and control at State Load Dispatch Center (SLDC), Gujarat. It is all about right load data, likely errors, probable cause, data filtration, data relations and effect of environment factors on system load. Hope the information will be useful to intending programmers.
Load Territory: Generally load assessment and forecast is required for particular area. The area may be Industrial Zone, City, District, Area of Substation or Licensee or Distribution Company, State Grid, Regional Grid or National Grid. Forecasting requires historical data because it provides characteristic of the load variations.
Load Assessment: The load of any area is not the summation of rating of connected load, contract load or operating load by all users in that area. It may be the actual power drawn by all users along with power wasted in the area. Practically it is rather than difficult, impossible to get correct data. In practice this is assessed indirectly. Total power consumed is total power delivered in that area. We use term load but actually we assess power required in the area and is referred to as power demand. This is what required for operation planning. Power delivered in the area is power injected by power generators within the area plus power received in the area from the other systems through inter connecting lines.
Injection by Power plants: This includes all power plants within area irrespective of its ownership. Power injection is power generated less its own auxiliary consumption. Sometimes gross generation is considered as power injection. But it is not correct because auxiliary consumption is associated with generating unit. Whenever generator is out its auxiliary consumption is also absent. Generator producing 500 MW has about 45 MW of auxiliary consumption. So its injection will be 500-45=455 MW. If this generator is taken out, the increase in power import will be 500-45=455 MW only. Therefore auxiliary consumption of generator cannot be considered a part of system load.
Injection by Tie line: All the power systems are interconnected operating in the grid mode. Power flows from/to other systems through interconnecting lines depending upon systems parameters. Metering is available at both ends of the interconnecting lines. Power received/sent (imported/exported) at the point within the area is considered as positive/negative injection for the load calculations.
Average MW: Normally load is expressed in MW and is measured by indicating meters. The meter shows instantaneous value of power flow. MW meter reading continuously changes. Noting and summing up of these readings may lead to inaccuracy due to time diversity in meter reading. There may be other issues also depending upon whether meter is analogue or digital. In case of analogue meter, zero setting and meter reading errors are expected. But average MW over the time slot represents accurate average load. Average MW is derived from energy meter readings. Difference of MWH meter reading over the time slot divided by time interval in hours will be the average MW for that time slot. MWH reading difference for 15 minutes time slot is to be divided by ¼ i.e. multiply by four is average MW for that slot.
MWH Logging errors: Some errors are likely while operator is reading the meters and recording. So demand derived from such data is not accurate. So also forecast based on such data cannot be accurate. The errors may be due to the following reasons.
Diversity in meter reading: There may be many control panels at station and each panel may have many meters for various parameters. Operators note down readings of all the meters at prescribed intervals. Operators read and log all meters one by one moving panel to panel. It takes some time in completing the round. Assume that operator is following the same sequence in each reading cycle. In that case reading intervals may be equal but data is time staggered. Load derived from such reading has no significant error during the steady load period. But it leads to significant error during the period of load rise and fall. Calculated load is more than actual during rising load period and it is less than actual during dropping load period. This error can be minimized by meter readings in two rounds. The first round may be only for selected MWH meters reading followed by second round as usual for all other readings.
Out of schedule reading: Normally readings are noted hourly. Operators have tendency to start data logging early in last hour of the shift so as to complete his work before leaving his duty. Obviously the effect is lower average MW in this hour and higher average MW in next hour. This type of error can be eliminated by shift change at mid of the hours.
On line Data: Most of the power systems have SCADA system. Normally digital data like breaker and isolator indications, protection system indications and analogue data of frequency, voltage, active power, reactive power and current flows are scanned. Availability of energy counters in SCADA system can be useful for the purpose. All these data are updated periodically and snapshot of required data can be best alternative to derive the load. Only validity has to be ascertained for accuracy and regular updating.
Demands: Load forecast is advanced estimation of future demand. Demand is power injected in the system derived as discussed here above. Demand is of four types as under:
Catered Demand: The sum total of power injection in the system by various sources is catered demand. The data of catered demand is mutilated due to variation in frequency and load shedding. Therefore catered demand data have no consistency and do not represent real system demand. It only represents how the power is delivered in the system over the day.
Computed Demand: Power drawn by frequency dependant loads in the system varies with the change in frequency. System frequency depends on power supply demand condition in the grid. System frequency may not be normal all the time. Frequency variation do not follow any regular pattern and hence unpredictable. The catered demand derived as above has deviations from true demand due to frequency dependant loads.
So catered demand has to be corrected for each time block using formula as under:
Computed Demand = Catered Demand +b×dF
Where b is system Bias and dF is frequency deviation for corresponding time block
System bias may be 3 to 4% per Hz of catered demand. (Bias depends on mix of load types)
Frequency deviation = (Nominal frequency – Average Frequency during time slot)
Computed Demand represents all load data at nominal frequency. This frequency correction can be negative also when block frequency is above normal.
Restricted Demand: Computed demand data corresponds to nominal frequency. But for system security and adhere to operating discipline, occasionally real time load shedding has to be imposed according to requirement. Load not so catered reflect as load drop and not represent the true demand. Therefore, correction is required for concern time blocks as under.
Restricted Demand = Computed Demand + Load not supplied.
This data of restricted demand represents routine load pattern of the system at nominal frequency. Therefore data of restricted demand is used for Short Term Load Forecasting. Forecasted demand is useful for operation planning. Unit Commitment, Generation Scheduling (with merit order), ISGS Power Indent and Power Purchase from other sources is decided based on forecasted demand and estimated on line availability.
Unrestricted Demand: This represents power requirement in the system when there is no type of constraint on use of power by category of consumers. It is always the aim of supply authorities to cater unrestricted demand but anyhow not feasible due to various reasons. So, to optimize utilization of available resources in best way, some statutory restrictions are enforced on various categories of consumers. Unrestricted demand is calculated from restricted demand by modifying respective time slot as under:
Unrestricted Demand = Restricted Demand + Load Relief due to statutory restriction in the time block.
This unrestricted demand is useful for long term forecast for power system expansion planning etc.
Statutory restrictions may be similar to hereunder.
Load Stagger: The objective of this is to flatten the load curve so that maximum load can be catered with available on line capacity. Period of power requirement of various types of loads is different and operating hours are also different as per convenience. So, unrestricted demand curve is in not flat but load varies over the time of the day. Load factor of such unrestricted demand may be in the range of 70 to 80% depending upon load mix. The load requiring only few hours power in a day can be shifted from high demand period to low demand period. Load for this purpose is selected such that it causes least inconvenience to minimum users.
Holiday stagger: Industrial power users have to observe weekly holiday as per statutory requirement. On holiday the power demand drops considerably and there is unused on line capacity requiring back down. Some time it may require stopping generator. By staggered holiday this weekly drop can be distributed on all the seven days of week. So, load on all weeks is equal but reduced by 100/7 = 14% of weekly drop. All the industries observing weekly holiday are divided in seven groups having almost equal loads drop. Each group has to observe weekly holiday on different day fixed for the each group. Fixing different holiday may be based on some base like territory, category, etc. So, exact load balancing in all groups is not feasible. So, load drop on each day may not be exactly 14% but can be around this. This is soft restriction because industries continue to work six days in week without any production loss.
Recess stagger: Industries working round the clock has three shifts of eight hours starting from mid night. They have to observe recess in the mid of the shift as per statutory requirement. Recess in afternoon shift may be during evening peak hours. Evening peak load is the highest of the day and is critical period for system management. All the resources are on during this period to meet the demand without drop in frequency. But load drop due to industrial recess during this period causes sharp drop in demand and frequency shoots-up very high. After the recess, load is resumed and demand shoots-up and frequency drops. Similarly system demand is bare minimum between 03 to 05 hrs early morning and frequency is high. Load drop due to industrial recess of night shift during this period cause frequency rise very high. This phenomenon can be mitigated by staggering of the industrial recess. Huge load drop for short period can be stagger by making three to four groups of industries and fixing staggered recess timings. By this arrangement power requirement during peak hours has some relief as shown in the figure and abrupt frequency change is avoided. This is also a soft restriction.
Agricultural load stagger: Power required for agricultural pump sets is for some hours in a day. Numbers of persons engaged per MW is very less compared to other category. Users have choice for operating hours but can be operated at other time also. Fulfilling power requirement in low demand period instead of peak demand period is very helpful for managing peak demand. Designing of power supply schedule for this is very tricky because all users are to be treated at par with different requirements.
Peak Restriction: Many small and medium industrial units are working in general shift. Restricting use of power during evening peak hours is helpful in managing peak demand. Such users can manage their working hours accordingly.
Urban load Shed: This is implemented in acute power shortage when it may be difficult to provide minimum requirement of power to any category. It is arranged such that day, duration and time slot of the shedding has least inconvenience.
Forecasting Period
Long and Medium Term: This is useful for power system development planning, monitoring, reviewing, rescheduling of execution, material management etc. Unrestricted demand is useful for the purpose. The forecast is cannot be fully analytical using any extrapolative methods because it is also influence by factors other than past data. Factors like government policy, economic situation, industrial development, development of ancillary facilities etc., may change the trend.
Short Term Forecast: This forecast is mainly useful for power system operation planning. Restricted demand is useful for the purpose. It can be analytical using any extrapolative methods. It is not influence by other factors like long term forecast. But environmental parameters have impact. Forecasting period is less than a year.
Yearly: Yearly load forecast is useful for planning of network and generators statutory/schedule outages for boiler inspection, annual maintenance, capital overhaul, modification, upgradation etc. Forecast of only Peak/Off peak demand and energy requirement is sufficient for the purpose.
Weekly: Weekly forecast is useful to plan outages of network and generator for urgent work. It is also useful to review/modify restriction schedule in view of change in load pattern on account of seasonal change.
Next day: This is most useful and used load forecast. Short Term Load Forecast (STLF) is referred for this category. It is used for operation planning activities like unit commitment, generation scheduling, ISGS power indents, power purchase/assistance from other sources. It is also useful for planning short term control measures while acute shortage of estimated OLC.
Run time or Corrective: This load forecast is for the current day. Forecast and actual load is monitored in real time for any deviation. Consistent deviation indicates change in load due to unexpected abnormal condition. Decrease in demand may be due to shutdown of major industries on account of strike, breakdown, fault, etc. Increase in demand may be due to failure of captive plant of major industry. This forecast is for remaining period of the day. This is useful to take corrective action for rescheduling power input in the system.
Forecasting Day: Hourly load forecasting is most common. Load is forecasted for next day on hourly bases from 00 hrs to 24 hrs. But this is not proper. Loading pattern of most of consumers is almost similar on all days of the week. But loading pattern of industrial, commercial and office is different on holidays. Industries working round the clock start first shift at about 07 or 08 hrs in the morning. Similarly, some other loads like general shift working industrial units, shops, malls, offices, banks and many others starts after 07 hrs. That means load variations due to holiday starts from 07 hrs and continues till next day morning 07 hrs. Therefore, load forecast should be from 07 hrs to 07 hrs of next day.
Forecasting Slots: Very simple load forecasting was manual and was block wise like peak load period, moderate load period, minimum load period etc. Normally, system load is calculated hourly. So, load forecasting is also hourly. But load variation is more evident when time interval is smaller. Now after implementation of Availability Based Tariff (ABT), most of the data for power transfer, generated etc., are available for 15 minutes blocks. So, it is feasible to have system demand data in 96 blocks of 15 minutes – and so also load forecasting accordingly.
Therefore ideal Short Term Load Forecasting will be in 96 blocks of 15 minutes from 07 hrs to 07 hrs next day.
Load Characteristics: System load is composed of various types of loads. Quantum and characteristic of each type of load is different. Basically loads can be categories according to operational characteristic are as under:
- Load throughout the day.
- Three shift Industries, Cold storage, Refrigerator, etc.
- Loads related to time of the day.
- Banks, Offices, TV, Shops, Cinema, etc.
- Loads related to day/night period i.e., sunrise/sunset.
- All Lightings, Street Light, Display, etc.
- Load for season only.
- Ice Factory, Oil Mills, Cotton Processing, Agricultural, etc.
- Load related to weather/climatic conditions.
- Fan, AC, Heaters, etc.
So, power demand varies over the day and year. Power demand is high when more loads incidents on the system. During evening hours all types of the loads are ON except some office loads. So, it is the period of highest demand of the day known as peak hours. Some loads are fixed with the time and some loads shift in time with season. Ultimately in particular season, these loads co-incident resulting in higher but shorter peak. In other season these loads have some diversity resulting in comparatively lower but longer peak. This seasonal change occurs due to variation of different loads and its time shift. This change is smooth over days and hence not visible in day to day observation. But fact is revealed if compared load curves of extreme day of seasons.
Seasonal Forecast: Daily load curve for winter, summer and monsoon are different. Sometimes season-wise forecast package is recommended. But it is not logical. System load is result of all types of the loads as above having different operating characteristics influence by various factors. Various types of loads may increase or decrease, duration of operation may vary and operating time also shifts. Ultimately load curve is changing. But all these changes occur gradually day by day. During long day of summer, various loads may be scattered resulting in longer but lower peak in morning and evening. But during short day of winter, some of the loads overlap resulting in shorter but higher peaks. Therefore, separate module is not required but same module should take care of gradual changes and ultimately seasonal changes.
Modern forecasting module use AI: When data relevance for future trend is unknown, fuzzy logic may be the solution. But if data relations are known, it would be better to link it for forecasting process. The results will be more accurate due to logical approach. In absence of any known link, program will try to establish links with all the data and sometimes have erratic result. Here are some hints for relationship.
Day pattern: Load data of any hour has relation with previous block and following block. Strongest attachment is with adjoining block (previous block and next block) and weaker attachment with block before previous block and block after the next block and so on. H hours load has strong attachment with H-1 and H+1 hours load but weak attachment with H-2 and H+2 hours load. Similarly H-3 and H+3 hours load has weaker attachment and so on.
Forward Trend: Load trend is derived by comparing load of corresponding blocks of previous days. The difference reveals rising or dropping trend of the load. It also indicates the type of trend whether is uniform, growth, exponential etc. This trend is useful for forecasting.
Weekly trend: System load data with staggered holiday requires different treatment. All consecutive days load differences as seen earlier in holiday staggering. Therefore, the consecutive days load data is not suitable to get load trend as above. In such cases load trend is derived comparing same block load with corresponding day of last week. These differences are expected to be large due to weekly difference. Load differences of all week days with corresponding day and block of previous week is useful to ascertain load trend for forecasting.
Power system facing acute power shortage has to implement regular load control measures. Power supply to various categories and/or clusters like agricultural, villages, towns, talukas, districts, industrial zones or area of city is selected to switch off power as per schedule. The schedule may be for few hours every day or more hours once in a week. Some category of consumers observe different holidays on their own like saloons and parlors on Saturday, shops on Monday, offices on Sunday etc. In such case daily load curve may not be similar. So, load forecast should follow above logic of weekly trend as in staggered holidays.
Load variation on festival holidays is different for each festival. On HOLI, the load is normal till evening and drop start thereafter but DHULETI is full light load day. JANMASTHAMI has no load drop but next day is light load day. In Gujarat DIWALI days load drop starts in the evening and minimum load on next day. Load growth thereafter is day by day and normal load reach after 5 days. Hence, load forecast for such festival holidays can be derived from similar holidays in previous years.
Generally understood that larger the past data, better is tuning/training of program and forecast will be perfect. This may be true for other matters but not right for load forecast. As discussed above, the load pattern is continuously changing and very old data have no significance and may lead to errors. Only three to four weeks immediate past data should be used for forecast.
Data Filtration: Past load data is used as base for forecasting. Forecast may not be perfect if input data is not proper. Manual data can have errors of reading, logging, transferring, calculating etc. Online data can have errors due to calibration of sensors or its supply failure, noise on channels or transmission failure, etc. Perhaps errors could have been rectified at the data sources. However, data has to be filtered / rectified before processing. Logic at 9 above can be used for the purpose.
Accuracy Check: Hundred percent perfect forecasting is not possible. Small deviation is expected and is allowable. But wide deviation in forecast and actual load indicate inappropriate data, filtration or forecast. Therefore, validity check is important before implementation in the field. Forecasted load data is compared with actual load data and deviation is found for each block. Validity check should be performed for numbers of days to ascertain consistency of results. Various ways for validity check are as under:
Simple Average: This simple average of deviation in all blocks of the day. Positive and negative deviations cancelled in this method. So, it is not a good check method.
Maximum Deviation: Small deviation is expected in any forecast and is immaterial in operation planning. But wide deviation will mislead for operation planning. So the maximum deviation is considered as validity check. Sometimes maximum positive and maximum negative deviation is also checked. But in this case other wide variations remain unnoticed under the cap of maximum. So, it may not project the true accuracy of the program. This method is not perfect but better than above.
Unsigned Average: Average deviation is derived from unsigned deviations of blocks of the day. Here positive and negative deviations do not cancel. This is moderate method for accuracy check.
RMS Deviations: RMS is root mean square of deviation in all blocks of the day. This is presented in percentage of average actual load of the day. This is the best accuracy indicator of forecast.
For training / tuning and accuracy check huge data base is used. For example, load for Thursday is forecasted using actual load data up to Wednesday as input. Than forecast of Thursday is compared with actual load of Thursday and error is derived. Next load for Friday is forecasted using actual load data up to Thursday as input. Than forecast of Friday is compared with actual load of Friday and error is derived. In this way past load data base is build up. Here gap is of one day between latest actual load data available and day of load forecast.
But field implementation differs because current day actual data is not available. Forecast for tomorrow is done using actual load data of yesterday. For example, on Wednesday load is to be forecasted for Thursday. But actual load data available is up to Tuesday only. So there is gap of two days between latest actual load data available and load to be forecasted. Therefore this aspect is to be considered in load forecast program for field implementation.
Environmental Effect: Forecast procedure discussed above is without consideration of impact of environmental variations. Following three parameters have influence on the power system demand.
- Temperature
- Wind Speed
- Rainfall
Ambient Temperature: There is no appreciable variation in system demand during normal temperature range. But system demand will increase when temperature is higher or lower than the normal range. Ambient temperature influence system load in two ways:
Cooling load: The cooling load is added when ambient temperature rises above normal. It is due to operation of Fans, Air Coolers and Air Conditioners. This load varies with variation in temperature. There is no cooling load up to certain temperature. Thereafter, cooling load increase slowly with rise in temperature up to a limit because very few consumers use cooling devices in this temperature zone. But cooling load increase faster when temperature rises beyond the limit – when more and more consumers use cooling devices. Again cooling load growth is slow after certain high temperature because all the installed and available cooling devices are in service at this stage. Beyond this stage load increase is slow with further rise in temperature. This is because no more gadgets are added but is due to long operation of compressor in ACs, fans at high speed and reduced diversity of operation of cooling devices.
Heating Load: This load is added when ambient temperature drops below normal. It is due to operation of heaters. This load varies with variation in temperature. There is no heaters load up to certain drop in temperature. Thereafter, heaters load increase slowly with drop in temperature because very few consumers use heaters in this temperature range. But heaters’ load increase faster with drop in temperature beyond the limit – when more and more consumers use heating devices. Thereafter there is no appreciable rise in heater loads – because all the available heating devices may be in service. However, meager load increase is observed with drop in temperature because operation of heaters may continue.
This load is area specific. Cold zone has only heaters’ load, whereas hot zone has only cooling load. Other areas have both loads.
Implementation: Normal load forecast includes cooling and heating loads. Because past data used for forecasting is inclusive of respective load as per season and area. Therefore no correction is required in forecasted load for routine temperature change. However, correction can be applied to forecast when temperature variation is abnormal. Exact temperature load relation can be established from past incidences. The correction can be positive or negative depending upon the temperature drift. This method is applicable for cooling as well heater load.
Wind speed: Wind affects the above loads but effect is different. During hot days, operation of fans, coolers and ACs reduce during windy atmosphere as people opt to enjoy natural breeze avoiding use of artificial cooling device and also to save energy. But during cold days, operation of heaters increases during windy atmosphere.
Rain: Rain fall causes drop in demand. Demand drops due to two reasons:
- Fault on distribution feeders. System demand drops due to loss of load on account of tripping of distribution feeders on fault. Drop is large during first rain of the season. Load drop on this account continues till feeders remain out for fault rectification.
- Non operation of agricultural pump sets. After natural irrigation by rain lift irrigation by pump set is not required. So all the pump sets in the area of rain fall will be out and load drops. Rain is local event and hence its effect on load drop is also local. Therefore, rain data from numbers of strategic points all over the area is required. It is a task to get all past, present and future rain data in time from all these points. Pumping load may re-incident in case raining pattern is not as per irrigation cycle required according to the crop in the concerned area.
Agriculture load pattern is related to type of the crop in the area. Each crop irrigation requirement is peculiar. Also, user characteristic too differs. Farmers prefer to run pump sets in the evening hours during hot summer days for their convenience and also to have less water for evaporation. But they prefer to run pump sets during noon time in the winter. However, farmers in the fields near the forest, prefer to work during day time irrespective of the season because of fear from wild animals during night hours. Farmers in the area like Saurashtra where water is deep and inflow is slow, cannot operate pump sets at a stretch. They have to operate with intervals.
Proportion of agricultural load and other load have wide difference in different areas. There is wide difference in percentage load drop due to rain in different areas. Load drop due to rain may radically change power flow pattern in the system. Network loading needs close monitoring during the season. So load forecast is very complex due to erratic behaviour of users, erratic rain and erratic load drop. Therefore very accurate load forecast for monsoon is not feasible.
Conclusion & Future Trends
Load forecasting is the basic step in planning of Electric Power System. In an interconnected power system, load forecasts are usually needed at all the important load buses and systems. A great deal of attention has in recent years been given to the question of setting up the demand models for the individual appliances and their impact on the aggregated demand. It may often be necessary to make use of non-linear forms of load models and the question of identification of the non-linear models of different forms is an important issue.
Finally, a point may be made that no particular method or approach will work for all utilities. All methods are strung on a common thread, and that is the judgement of the forecaster. In no way the material published here is exhaustive. The intent has been to introduce some ideas currently used in forecasting system load requirements.
Forecasting electricity loads had reached a comfortable state of performance in the years preceding the recent waves of industry restructuring. Adaptive time-series techniques, based on ARIMA, Kalman Filtering, or spectral methods are sufficiently accurate in short term for operational purposes, achieving errors of 1-2%. However, the arrival of competitive markets has been associated with the expectation of greater consumer participation. Overall we may identify the following trends.
- Forecast errors have significant implications for profits, market shares, and ultimately shareholder value.
- Day ahead, weather-based, forecasting is becoming the most crucial activity in a deregulated market.
- Information is becoming commercially sensitive and increasingly trade secret.
- Distributed, embedded and Dispersed Generation (DG) may increase.
References
[1] Bunn, D. W., “Forecasting Loads and prices in Competitive Power markets”, Proc. the IEEE, Vol. 88, No. 2, Feb 2000, pp 163-169
[2] Pabla, A. S., Electrical Power Systems Planning, Macmilan India Ltd., New Delhi, 1998.
[3] D. P. Kothari, I. J. Nagrath, Modern Power System Analysis, Third Edition, Tata McGraw-Hill, New Delhi, 2011.
Er. Natvar D. Makwana is a retired senior engineer served last 26 years on various positions at state load dispatch centre and post retirement 7 years as visiting faculty of Parul Institute of Engineering and Technology. He has functioned as a member at various taskforces/committees of state/regional/national level related to power system operation. He is Fellow of the Institution of Engineers, India (IE) and Fellow of Society of Power Engineers (CBIP).
Paresh R. Modha has total work experience of more than 11 years. He completed his B.E. from A. D. Patel Institute of Technology, New Vidyanagar in the year 2009. He received his M.E. in Electrical Engineering specialized in Electrical Power Systems from Birla Vishwakarma Mahavidyalaya (BVM), Gujarat Technological University, Ahmedabad in 2011. At present he is perusing Ph D from CVM University, Vallabh Vidyanagar. He has worked as a Junior Engineer in 66 kV Wind farm of Suzlon Substation and then joined academic filed with reputed CHARUSAT University, Changa during his starting career. At present he is working as an Assistant Professor in Department of Electrical Engineering, ADIT, New Vallabh Vidyanagar.
