Demand Analysis - Science topic

Pleasure Negin Zarbakhsh

Choose a high number of lags and calculate a penalized model (e.g. using LASSO, ridge or elastic net regularization). The penalization should reduce the influence of irrelevant delays, allowing the selection to be done more effectively. And Experiment with various lag combinations and either.

Fisher's Points. One of the most popular supervised feature selection approaches is the Fisher score. The method we'll employ returns the variables' rankings in decreasing order depending on the fisher's score. The variables can then be chosen based on the circumstances.

Kind Regards

COVID 19 has no direct impact on the crops, but it diverge concentration of farmers towrds its care due to low income and fear.

You need to make sure the price data from Bureau relates to the same/similar products in the survey data for the same time period and for the same location. If you need to use proxies state this in your data and explain you were unable to obtain actual price data.

Good afternoon.

I recommend a recently published research on the state of the art of equivalent viscous damping.

Any questions are available

best regards

You can try R-studio Cloud or the colab by Google(Working with Python) for the same. Both are online. You can give the data source. So it will update the data as the given intervals.

Peterson Dayan I am not sure, if I understand your question. If you add demand to your main roadway this usually leads to more congestion. How is that meant to make a city more competitive? By designing the street network you can of course adjust how want to meet this increased demand: walking, cycling, public transport, cars...

You can satisfy more about DNA extraction method through counting specificity ,sensitivity and accuracy of the extraction method.

I have been doing what you seem to refer to as non linear modelling in transport demand analysis since 1975. More formally, the issue raised is that of the proper form of variables (and sometimes of functions) and I have adressed it with Box-Cox transformations, both direct and indirect, as you will see from three enclosed documents pertaining both to models of levels (generalization of classical regression) and logit models (discrete or aggregate) and to various algorithms to estimate the parameters and derive meaningful statistics from them. If you have questions after looking at those (available on ResearchGate in any case, but enclosed for convenience), let me know.

Dear Diego,

The short answer is that I don't know of any ideal solution here, and my advice is to be skeptical of those who claim to have the answer. I realize that is not the most helpful advice. Some things you can do instead:

- In your other (e.g. energy system) models, seek robustness to a range of outcomes for vehicle sales, rather than optimizing for a single growth path.

- Focus on ranges instead of point estimates, by varying key assumptions and parameters.

- Look at historical data on rates of growth. Is there a precedent for a new technology growing at the pace that is being projected? If not, ask yourself what is so different about this technology than every other technology before it. Here's one source on rates of automotive technology growth (also published as SAE paper 2012-01-1057): http://web.mit.edu/sloan-auto-lab/research/beforeh2/files/Zoepf_MS_Thesis.pdf

- For the 5-8 year timeframe, you can hunt down auto manufacturers' product plans and product cycles. If new EV models are entering the lineup, you can generate bottom-up estimates of ranges of EV sales, informed by historic market shares of new EV models. Then you can make some assumptions about growth over the remaining years.

The above can be helpful in judging the reasonableness of your estimates.

Hello Huiping, not sure how's your research going, but I agree that this is a great research idea. We have been studying the combination of industrial loads + energy storage to provide demand response together, where the industrial loads (power change is large but slow) provide the bulk part and the storage device (power change is small but fast) provide the fine part. Details can be found in our published paper. And we think it is interesting to investigate the combination of different loads to provide demand response, such that their advantages can support each other.

Thank you very much! I will get in touch with him.

I think this link will help you..

Refer my paper, " Micro processor based load shedding controller".

   

Have a look at this link :

In practice it is mainly done by using judgmental demand forecasting and adjusting the demand forecast resulted from a DSS. However, this is not a reliable approach and several studies have shown the fact that "adjustments not necessarily improve the forecast accuracy" .  

As far as I know there is no reliable decision-making tool as of yet that can capture these uncertainties in an optimum way or at least adjust the decision makers adjustments!

I hope my Ph.D. thesis finds a solution to this problem!

A paper entitled " Bounded Rationality in Newsvendor Models" has shed light into this topic I suppose.

Take a look at it. You might find it useful.

The problem is not just controlling inventory, but also should focus on service level. Weili, X., Xiaolin, X., & Ruxian, W. (2013). Combined Sales Effort and Inventory Control under Demand Uncertainty. Discrete Dynamics In Nature & Society, 1-8.  They tackle the problem of inventory from both sides. Very mathematical, but perhaps useful.

Another nice article is Yeo, W. M., & Yuan, X. (2011). Production, Manufacturing and Logistics: Optimal inventory policy with supply uncertainty and demand cancellation. European Journal Of Operational Research, 21126-34. (http://www.sciencedirect.com/science/article/pii/S0377221710007332)

The last article has many interesting sources which might help you further e.g. R. Güllü, E. Önol, N. Erkip. Analysis of an inventory system under supply uncertainty ,International Journal of Production Economics, 59 (1999), pp. 377–385

The number of hidden neurons is very syndicate since the hidden neurons are regarded as the processing neurons in the network, plus having a small number of hidden neurons can increase the speed of the training session whereas a large number of hidden layers can prolong the training session. There are two techniques for selecting this parameter, these are namely; growing technique via which the number of hidden neurons is selected as a small number then the number gets increased gradually. The other technique is called pruning via which the number of hidden neurons is selected as a large number and then it gets decreased via eliminating some insignificant components .  It is recommended to select the initial number of hidden in accordance with the equation one , then after, either the Growing method or Pruning is used to arrive at the best selection of hidden neurons so that to achieve promising results for the network.

hidden no= (input no. +output no.)/2                      eqaution one.

context no= no of hidden

Just for clarification, the Winkler titration for DO can be used as PART of the BOD measurement but you have to take  2 sets of replicate samples , one set has the DO measured at the start and the other set after 5 days. If the BOD is high you need to dilute (and re-saturate the samples by sparging with air). If the dilution is significant you may also have to seed with bacteria and add inorganic nutrients.

These days the titration method is usually replaced with a DO electrode which allows  you to do the  start and 5 day DO measurements on the  same sample.

Whilst your BOD for seawater does seem high I cannot think of a mechanism which would cause such a positive error other than a fault on the manometer.

One can draw up a typical electrical demand profile for a rural household and use this in a simulation type model. By shifting the loads in the mornings and evenings for one simulation, a number can be added, while noise can be added to the profile graphs to make it more natural.

A more realistic way is to use datalogged data of energy consumption patterns from a metering website or an onsite metering and weathers station solution. There are a number of rural schools and rural clinics that operate off-grid and these installations are typically fitted with a wireless remote monitoring and metering solution that saves the solar power generation and wind patterns for the site, see link below

With the Energylens platform, one can build demand profiles such as those illustrated on this link below

trust this helps

FC

"R" software is much easier than "STATA" in demand estimation and most importantly it is free. http://www.r-project.org/

There is a built in code in the Package ‘micEconAids’ in R. 

Suppose you have 4 commodities. Then the command for Stone price index would be like,

aidsPx( "S", c( "pFood1", "pFood2", "pFood3", "pFood4" ), nameData, c( "wFood1", "wFood2", "wFood3", "wFood4" ) )

where, S refers Stone price index, pFood1-pFood4 are the prices for the 4 commodities, nameData is the name of your data file and wFood1-wFood4 are budget shares of 4 commodities.

The answer depends on the industry and how far the peak is from the typical quantity demanded.  For example, trying to use price to encourage movement away from peak times for emergency ambulance service is unlikely to be effective. Alternatively, it seems to work for the airlines, hotels, electricity, and several others.  It works on golf courses, where players pay a premium to play on the weekends, and especially in the mornings to avoid relatively hot US temperatures in the middle of the day.

Let's follow the golf course pricing model for illustration here.  The course has a finite capacity each hour and the closer we get to it, the less golfers tend to enjoy it (e.g., they frequently encounter delays as others face challenges ahead of them)   I like to think about problems like this as a series of demand curves (e.g., demand for a round of golf beginning at 7:00 am  likely differs from the demand at 3:00 pm.

We developed a pricing model that increased the price for the most popular times, which incidentally exceed capacity by a significant amount.   We recommended lower prices when relatively few golfers began to play on the municipal course here (e.g., 2:00 pm in the summer when the temperature around here approaches 100⁰ F (i.e., 38⁰ C).  We expected less experiences golfers would not wish to pay premium prices and would be more eager to play at the lower price.  This should have improved the golfing experience for both groups by decreasing frustration of each. Few appreciate being hurried or slowed by others.   Even without the heat, peak load pricing can improve the situation.  

So then end result is more people able to play golf, a significant increases in the total revenue collected at the golf course, among other things.  We also proposed much lower fees for youthful golfers when the course was used the least.  This potentially would increase demand in the future to the extent they acquired more interest in the game.   

Elected officials were not excited about one aspect of this.  Can you guess? 

They were uncomfortable about the higher fees at peak times because they thought it sounded like a "tax increase."  

This is just one example that has some interesting implications.  The principle is the same and in theory in some industries, it could smooth reshape the load on an hourly or daily basis.  FedEx charges much more for Saturday service; the reasons should be obvious. On the other had, would an economist wait to call 911 for an ambulance when facing peak demand pricing?

The easyest is to compare "cigarette market" with "deflated" prices of cigarettes. This gives you a first idea. Then you might consider "tabacco market" and the "smuggled market". Results depend on the relative weight of of these different product in the overall consumption.

if you estimate in differences, you eliminate the intercepts - they are not identified, and any restriction on them is redundant. By the way, I do not understand why the restriction on the shares changes when you estimate the model in differences; to my understanding, the parameters should be the same.

I agree with Juan. Selecting a forecasting procedure is not an easy goal. I would recommend to have a look to the book "Forecasting: principles and practise" by Hyndman and Athanasopoulos,

and also to the M-Competition results, published by Makridakis and Hibon:

Best regards

Capturing useful energy from natural energy flows like sunshine, wind, moving water is a great concept. The technologies to capture this energy aren’t cheap, however, nor do they work equally well in all locations. Typically, it’s hard to generate a significant fraction of total electricity we use onsite.

Before investing a lot of time and energy into this credit, focus on energy efficiency and passive energy collection such as daylighting, natural ventilation, passive solar heating before investing in renewable energy systems. This work will probably pay off faster than renewable energy, and if you do invest in renewable energy, you’ll have a lighter load for it to carry