Electrical Analysis that identifies price spikes PYTHON


Over the last couple of years wholesale power has averaged £40 - £50/MWh but has demonstrated increasing volatility, particularly in peak demand periods. It is fairly widely understood that the price of power can at certain times jump to very high levels (both in the wholesale market and particularly in the balancing system). We want to understand what the impact of price spikes is on the average wholesale cost of fuel for domestic customers.


Attached are several data sets (in a single spreadsheet):

Cash-out SBP: Elexon data System Price (this is the price we pay for power at times when we have bought insufficient power on the wholesale market when we are ‘out of balance’. This should happen in a minority of time periods).

APX Day Ahead Auction: Hourly delivered price through the day-ahead auction

Domestic Consumption: typical half hourly domestic usage patterns

Feel free to use any, all or none of this data or you can pull other data that you think is useful to provide insight around this question.

The Task

Please prepare some analysis that identifies price spikes (your definition) and determines what the impact on the average cost of power is during the period. What impact might this have on domestic customer bills? How might you use this information?

Analysis can be performed through any appropriate method and results can be presented in any format that you like. We will expect you to provide an answer and talk us through the approach you took, including any decisions that you made in getting to an answer. Also, any conclusions that you might draw having been through this exercise.

Provide graphs and analysis and results in Python (Jupyter Notebook)

Evner: Elektrisk Ingeniørarbejde, Elektronik, Matematik, Python, Statistikker

Se mere: data analysis using R or python, Need to finalise design for sport gel pack. 1. Please see the gel pack with the red background as that is the design style 2. P, statistical analysis help price, real time peak detection, peak detection algorithm, simple algorithms for peak detection in time-series, electricity price forecasting using artificial neural networks, electricity price forecasting models, detecting spikes in data, spike detection python, time series spike detection, electronics, python, electrical engineering, mathematics, statistics, price per hour of a freelance senior engineer in the usa, identify specialists you feel can help you through the advertising process of outsourcing the task to them, acca research analysis project price, scrape price data python

Om arbejdsgiveren:
( 0 bedømmelser ) London, United Kingdom

Projekt ID: #17148234

9 freelancere byder i gennemsnit £23 på dette job


Hi bro. I have read your description very carefully and i am so interested in your project. I am confident in your project and I can finish it clearly on time. I am well experienced and skillful Python programmer. Flere

£18 GBP på 1 dag
(7 bedømmelser)

Hey I am data scientist. i have 3 Years or more expereince in this field. i have done number of projects in machine learning as well in python(Django). 1) OCR ( human handwritten Recognition) 2) Object detection 3) fau Flere

£19 GBP på 1 dag
(4 bedømmelser)
£13 GBP in 3 dage
(0 bedømmelser)

Maybe i can help you with your problem sir. Provide graphs and analysis and results in Python (Jupyter Notebook)

£33 GBP på 1 dag
(0 bedømmelser)
£18 GBP på 1 dag
(2 bedømmelser)

hi am an electrical engineer and a data scientist, would you want to use my expertise

£55 GBP in 7 dage
(13 bedømmelser)

Hi, I have graduated from IIT Kharagpur and have profound experience in data analytics(using python, R, tableau) and automation using python. Please visit my github([login to view URL]) for my python ongoing Flere

£20 GBP in 3 dage
(6 bedømmelser)

Being an experienced BI professional and currently working on python there are various options to analyze the data sets and come to conclusion. Thanks for providing the details about your requirement. It would be great Flere

£15 GBP in 5 dage
(0 bedømmelser)
£13 GBP på 1 dag
(0 bedømmelser)