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How do you factor in external variables for your energy data analysis?

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Energy data analysis is a crucial skill for any energy manager who wants to optimize the performance, efficiency, and sustainability of their energy systems. However, energy data analysis is not a straightforward process that can be done in isolation. You need to factor in external variables that can affect your energy consumption, production, and distribution. These variables can be environmental, economic, social, or technical, and they can vary depending on your location, sector, and scale. In this article, we will discuss how you can identify, measure, and account for these external variables in your energy data analysis.

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Krishna Praveen Rao
Gian Cavigelli

Ganzheitliche Energiel?sungen für Private.

1 What are external variables?

External variables are any factors that can influence your energy data but are not under your direct control or measurement. They can be natural, such as weather, seasons, daylight, or geography, or human-made, such as market prices, regulations, policies, customer behavior, or technological changes. External variables can have positive or negative impacts on your energy data, depending on how they interact with your energy system and your goals. For example, a sunny day can boost your solar power generation, but also increase your cooling demand. A carbon tax can increase your energy costs, but also incentivize you to reduce your emissions.

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AYOUB BABA

Energy and Climate Energy Consultant
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Il est important de tenir compte des variables externes pour obtenir des résultats d'analyse plus fiables. En intégrant ces variables indépendants dans notre analyse, on peux identifier les facteurs qui influencent réellement la consommation d'énergie en tenant compte de son impact périodique, et on peux développer une corrélation efficace afin de bien gérer la consommation d'énergie relatives à sa variation et détermination d'incertitude de mesure lié à ses variables

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2 Why are external variables important?

External variables are important because they can affect the accuracy, reliability, and validity of your energy data analysis. If you ignore or overlook them, you might end up with incomplete, misleading, or erroneous results that can compromise your decision-making and your energy management strategy. For instance, if you compare your energy consumption across different months without considering the seasonal variations in temperature and daylight, you might miss the opportunities to improve your heating and lighting efficiency. Similarly, if you project your energy savings based on historical data without accounting for the changes in market prices or regulations, you might overestimate or underestimate your return on investment.

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3 How do you identify external variables?

In order to factor in external variables for your energy data analysis, you must first identify them. This requires a comprehensive comprehension of your energy system, data sources, objectives, and context. To identify external variables, you can brainstorm potential variables based on experience and knowledge, or consult other stakeholders. You can also research relevant literature or use online tools to uncover trends and patterns. Additionally, you can use statistical or graphical techniques such as correlation analysis or regression analysis to examine the relationship between your energy data and potential external variables. Machine learning or AI tools, such as anomaly detection, can also be utilized to detect outliers or deviations in your energy data that may be caused by external factors.

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4 How do you measure external variables?

The second step to factor in external variables for your energy data analysis is to measure them. This involves collecting, processing, and storing the data of the external variables that you have identified. You can use a variety of sources and methods to measure external variables, such as sensors, such as thermometers, anemometers, or photometers, to measure physical or environmental variables; smart meters, smart devices, or smart grids to measure energy-related variables; databases, such as government databases, industry databases, or academic databases, for economic, social, or technical variables; and web scraping, APIs, or RSS feeds for online sources. Additionally, you can use surveys - questionnaires, interviews, or polls - to gather the data of behavioral or attitudinal variables; and feedback, reviews, or ratings to obtain the data of the external variables from customers, users, or stakeholders.

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5 How do you account for external variables?

The third and final step to factor in external variables for your energy data analysis is to account for them. This means adjusting, normalizing, or modeling your energy data in relation to the data of the external variables you have measured. Various techniques and tools can be used for this purpose, such as adjustment methods like scaling, weighting, or indexing to modify the energy data for changes or differences in the external variables. Normalization methods like per capita, per unit, or per area can be used to standardize the energy data and eliminate the effects of the external variables. Additionally, modeling methods like linear, nonlinear, or multivariate can be used to estimate or predict the energy data based on the external variables. For example, a regression model can be used to estimate energy savings based on weather, price, and customer behavior, while a neural network model can be used to predict energy demand based on season, time, and occupancy.

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Gian Cavigelli

Ganzheitliche Energiel?sungen für Private.
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Um den W?rmeverbrauch effektiv zu bewerten, arbeiten wir mit HGT (Heizgradtagen). Diese gestatten Rückschlüsse auf den klimabedingten Heizenergieverbrauch. HGT sind definiert als die Summe der t?glich ermittelten Differenzen zwischen einer angestrebten Raumlufttemperatur von 20 °C und der Tagesmitteltemperatur aller Heiztage in einem Jahr. So kann berechnet werden ob es eine Energieeinsparung infolge des milden Klimas gegeben hat oder ob die Anlage optimiert wurde.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Krishna Praveen Rao
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We are talking about Variables and Static Contributors to Energy Data Analysis. When we take the examples of Weather and Seasons as External Factors, it is necessary to differentiate between them. The reason is obvious.While Seasons are fixed & repetitive in nature, weather within a season is variable. It is better to consider seasons as static and weather as variable. This way one can fix a projected consumption for a season. It will also help to plan out the Production better

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How do you factor in external variables for your energy data analysis?

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1 What are external variables?

2 Why are external variables important?

3 How do you identify external variables?

4 How do you measure external variables?

5 How do you account for external variables?

6 Here’s what else to consider

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