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Highlights
- The surge in availability of granular electricity consumption data from smart meters has made it easier to develop methods and algorithms to extract insights with varied approaches on how to do this.
- Machine learning or signal processing-based techniques can be applied to analyze smart meter data collection at different data granularities and identify the appliance-wise electricity load from historical data or meta-information.
- Leveraging the right techniques for smart meter data analytics simplifies demand-side energy management by enabling more cost-effective balancing of supply and demand.
In this article
In this article
Introduction
The focus on clean and affordable energy is driving the adoption of renewable energy and increasing reliance on distributed energy resources (DERs).
While this is an important step toward a carbon-neutral future, it also results in a volatile energy supply due to the intermittent nature of DERs. When combined with demand-side elasticity, the expansion of DERs can create a systemic imbalance in energy demand and supply. Granular smart meter data can be used to gain insights into consumption patterns to enable more effective demand-side energy management, leading to significant energy savings. Techniques such as non-intrusive load monitoring (NILM) extract insights such as household appliance usage behavior, and neighborhood comparisons of appliance energy consumption offer valuable inputs for sustainable energy management. It can also aid utilities by mitigating peak-load consumption while providing energy and cost-saving recommendations.
Improving energy delivery and management
DERs support the proactive consumer or prosumer with on-demand services and enhanced self-sufficiency.
Distributed energy generation is expected to account for 10% of the world’s global installed power generation by 2030, and this growth will be dominated by solar photovoltaic. However, the expansion of DERs and the associated deregulation of electric supply system creates imbalance in energy supply and demand. In this context, demand-side energy analytics help improve energy delivery and management. It analyzes energy consumption requirements and provides energy-saving recommendations and interventions across the demand chain. Smart meters provide access to household or building-specific electricity consumption data. With improved availability of granular data, consumption pattern insights can be used to ensure smooth electricity supply by mitigating peak-load consumption and unnecessary power breakdowns.
This paper highlights how smart meter data analytics techniques such as load disaggregation, forecasting, and energy consumption benchmarking can help multiple players in the utilities and energy markets toward efficient and sustainable energy management.
Ensuring a healthy demand-supply energy chain
The rise in energy demand is caused by increased use of electrical appliances throughout the year.
The unpredictable and intermittent nature of DER power generation, which is dependent on weather parameters such as solar irradiation and wind speed, creates volatility in the supply side of the electricity market. This along with demand-side elasticity makes it challenging to ensure reliable electricity supply. Smart meter data analytics can enable utilities to assess demand-side energy consumption and design interventions to ensure a smooth supply-demand chain. Tools have been developed to assess appliance-specific energy consumption, monitor appliance health, profile load, segment customers, etc.
DERs (Distributed Energy Resources) usually refers to small hydro, solar PV, and wind power systems on the demand-side of the electric utility system.
The following factors are critical for demand-side energy management:
Knowledge of peak load hours in localities of interest to design appropriate interventions
Aggregate forecasts of energy consumption to optimize electricity supply-demand chain
Customer segmentation based on consumption patterns
Customer consumption breakdown to aid targeted demand response
Four techniques to manage energy demand smartly
The surge in availability of granular electricity consumption data from smart meters has made it easier to develop methods and algorithms to extract insights.
These include per-household appliance usage behavior, aggregate electricity consumption estimates, and neighborhood comparison of appliance energy consumption behaviors. Such in-depth insights are critical to assess local electricity consumption to manage energy better.
Here are four approaches to extract these insights from smart meter data:
Electrical load disaggregation: Load disaggregation helps consumers understand energy spent per appliance in the household or building across different durations (day or month-level) depending on the smart meter data granularity. It can also help design optimal demand response and peak load shaving schemes through billing and protocols such as load scheduling of high-power appliances.
Machine learning or signal processing-based techniques can be applied to analyze smart meter data collection at different data granularities and identify the appliance-wise electricity load from historical data or meta-information.
An illustrative view of a non-intrusive load monitoring framework
An illustration shows three processes in a non-intrusive load monitoring (NILM) framework: pre-learning, house-specific learning, and appliance identification. NILM extracts household appliance usage behavior and provides insights with neighborhood comparisons of appliance energy consumption, offering valuable inputs for sustainable energy management. Here are the three processes described in brief featured in the graphic: 1.Pre-learning of appliance associations with smart meter data from publicly available datasets 2.House-specific learning to tune or customize the pre-learnt features or associations to the household of interest 3.Appliance identification to identify the appliances contributing to the smart meter data
There are three processes in non-intrusive load monitoring (NILM):
- Pre-learning of appliance associations with the smart meter data from publicly available datasets
- House-specific learning to tune or customize the pre-learnt features or associations to the household of interest
- Appliance identification to identify the appliances contributing to the smart meter data
Load disaggregation also reveals consumption load variations due to seasonal and weather conditions. In this approach, the large amount of historical data compensates for the low-granular smart meter data and provides more accurate disaggregated results using the NILM framework.
Load modeling and forecasting: A data-driven forecasting model can extract regressive patterns from the aggregate building smart meter data. By relying on information from sensors such as occupancy sensors, temperature sensors and so on, it can learn the energy consumption patterns.
Seen below is a simple depiction of the forecasting model. Using artificial intelligence (AI), the model can generate accurate load forecast estimations on a weekly, monthly, and quarterly basis.
Load modeling and forecasting to predict aggregate energy consumption for commercial buildings
A graphic depicting a forecasting model using artificial intelligence to generate accurate load forecast estimations weekly, monthly, and quarterly. The model can extract regressive patterns from the aggregate building smart meter data. It can learn energy consumption patterns using information from sensors.
Customer segmentation: Customer segmentation is the grouping of customers based on their average consumption, and the types of appliances they use at home. For instance, schedulable loads such as washing machines or elastic loads such as air conditioners. This segmentation helps understand energy requirement and manage energy demand.
Appliance health monitoring: Electrical load disaggregation enables tracking an appliance’s condition as time passes. The information can be used to offer more efficient, energy-saving.
Neighborhood comparison: Appliance consumption estimates can also be used to understand local consumption and change consumer behavior at a fundamental level when it comes to using (and saving) energy.
Electric vehicle disaggregation – an understated benefit
Apart from benefits like energy saving, budgeting, and managing demand, smart meter data analytics also helps with electric vehicle disaggregation.
The latter adds considerable load on the distribution side. Aggregators or retailers are unaware of this addition until it is discovered using technologies like load disaggregation. EV batteries have the potential to act as load or source depending on usage. EVs could be charged during off-peak hours (load) and could be utilized as a source during peak load hours.
Future of energy management
Global utilities are likely to increase investments in smart meter analytics by three times between 2021 and 2030 to optimize the management of DERs.
It will also help them tap the unrealized value of energy consumption data to provide energy-saving recommendations. Utilities and energy companies are also likely to continue their digital transformation journey with increased focus on AI and machine learning to manage high volumes and diversity of smart meter data. Analytics support interventions at various levels including aggregators, retailers, and consumers, to ensure a smoother, more balanced smooth supply-demand chain.
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