Deep Learning Based Demand Forecasting in Smart Grids

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This entry is adapted from the peer-reviewed paper 10.3390/s23031467

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role.

demand forecasting load forecasting demand response forecasting horizon smart grid smart environment Deep Learning Long Short-Term Memory networks Convolutional Neural Networks

1. Introduction

Electricity cannot be easily stored for future supply, unlike other commodities such as oil. This means that electricity must be distributed to the consumers immediately after its production. The distribution of electricity to final users has been done with the help of the traditional electrical grid (see definition in Table 1) which allows the delivery of electricity from producers to consumers. To achieve that goal, it connects the electricity generating stations and the transmission lines that deliver the electricity to the final users. Traditional electrical grids vary in size. When these grids started to expand, controlling them became a very complex and difficult task. Additionally, demand forecasting (see definition in Table 1) has not traditionally been considered.

Table 1. Important keywords for reading this entry.

Keyword	Definition
Demand forecasting	The process of estimating end-user demand in relation to electricity consumption over a given period.
Demand response	A change in end-users’ electricity consumption to better balance power demand and supply. Also referred to as demand-side response.
Load forecasting	The technique used by power companies to forecast the electricity needed to meet demand to better balance power demand and supply.
Load profile	The electricity demand forecast made for the next 24 h.
Microgrid	An aggregation of loads and microsources operating as a single system, i.e., a small electrical grid of users with local control and supply capability. It is normally connected to a centralised national electricity grid but can operate autonomously.
Traditional electrical grid	A grid developed specifically for the transmission of electrical energy from points of production to the utilization points. Also referred to as conventional/existing electrical/power grid.
Smart building	A building that uses a wide range of technologies to enable efficient use of resources, including electrical energy, while providing a comfortable environment for its occupants.
Smart environment	Any type of environment, such as a smart home or a smart building, that uses a wide range of technologies to enable efficient use of resources, including electricity, while creating a comfortable environment for its occupants.
Smart grid	An electrical grid that can monitor and provide real-time information on its own activity with the help of digital technologies. It is considered a developed form of the traditional electrical grid.
Smart home	A home that uses a wide range of technologies to enable efficient use of resources, including electricity, while creating a comfortable environment for its occupants.

In this context, the concept of the smart grid (see definition in Table 1) arises and starts to play an important role. This concept has been exhaustively reviewed in the literature (e.g., ^[1]^[2]^[3]^[4]). Smart grids provide a two-way communication between consumers and suppliers. Smart grids add hardware and software to the traditional electrical grid to provide it with an autonomous response capacity to different events that can affect the electrical grid. The final objective is to achieve an optimal daily operational efficiency for the electrical power delivery. In ^[4], the authors define “smart grid” as a new form of electricity network that offers self-healing, power-flow control, energy security and energy reliability using digital technology. In ^[2], the authors highlight that the concept of the smart grid is transforming the traditional electrical grid by using different types of advanced technology. According to these authors, this concept integrates all the elements that are necessary to generate, distribute, and consume energy efficiently and effectively. In ^[5], the authors emphasize that the smart grid concept emerged to make the traditional electrical grid more efficient, secure, reliable, and stable, and to be able to implement demand response (see definition in Table 1).

The smart grid paradigm allows consumers to find out their energy usage patterns. Consequently, consumers can control their consumption and use energy more efficiently. In the implementation of the smart grid concept, demand response—for both household and industrial purposes—plays an important role. Another useful tool is load forecasting (see definition in Table 1). In ^[6] the authors mark the importance of this concept in the context of smart grids, as forecasting the electricity needed to meet demand allows power companies to better balance demand and supply. Power companies are especially interested in achieving accurate forecasts for the next 24 h, which is called load profile (see definition in Table 1).

In addition, in recent years, the increased demand for electricity at certain times of the day has created several problems. Load forecasting is especially important during peak hours. Demand response encourages customers to offload non-essential energy consumption during these peak hours.

To face load forecasting challenges, it is necessary to use modern data-driven techniques. Indeed, the incorporation of new technologies, such as Big Data, Machine Learning, Deep Learning, and the Internet of Things (IoT), has upgraded the smart grid concept to another level, as these technologies allow for improved demand forecasting and automated demand response.

2. The Importance of Demand Forecasting

In ^[7] the authors summarize the main requirements of smart grids as follows: flexible enough to meet users’ needs, able to manage uncertain events, accessible for all users, reliable enough to guarantee high-quality energy delivery to consumers, and innovative enough to manage energy efficiently.

With these requirements in mind, smart grids should aim to develop low-cost, easy-to-deploy technical solutions with distributed intelligence to operate efficiently in today’s increasingly complex scenarios. To upgrade a traditional electrical grid into a smart grid, intelligent and secure communication infrastructures are necessary ^[4].

According to the study presented in ^[8], forecasting can be applied in two main areas: grid control and demand response. In ^[9], the authors highlight that forecasting models are essential to provide optimal quality of the energy supply at the lowest cost. In addition, real-time information on users’ energy consumption patterns will enable more sophisticated and efficient forecasting models to be applied. Forecasting must also consider the need to manage constantly changing information. In ^[10], the authors highlight that, with the smart grid, demand response programs can make the grid more cost efficient and resilient.

The authors in ^[11] remark that there are important challenges in demand forecasting due to the uncertainties in the generation profile of distributed and renewable energy generation resources. In fact, increasing attention is being paid to load forecasting models, especially dealing with renewable energy sources (solar radiation, wind, etc.) ^[9].

The distributed generation paradigm facilitates the use of renewable energy sources that can be placed near consumption points. When using this paradigm, smart grids have multiple small plants that supply energy to their surroundings. Consequently, the dependence on the distribution and transmission grid is smaller ^[9]. However, this paradigm makes grid control even more uncertain, especially when the distributed generation sources are renewable and consequently have a random nature. Despite this difficulty, the share in energy production of variable renewable energy sources is expected to increase in the coming years ^[12].

Another key element are microgrids (see definition in Table 1) ^[13]^[14]. Based on this concept, and taking into consideration the intelligence deployed in buildings, new concepts have emerged including smart homes (see definition in Table 1) and smart buildings (see definition in Table 1). Buildings today are complex combinations of structures, systems, and technology. Technology is a great ally in optimizing resources and improving safety. Advances in building technologies are combining networked sensors and data recording in innovative ways ^[15]. Modern facilities can adjust heating, cooling, and lighting to maximize energy efficiency, providing also detailed reports of energy consumption. In these new smart environments (see definition in Table 1), sensors and smart devices are deployed to obtain enough information about the users’ energy consumption patterns. Once again, this requires forecasting models that must be applied to the specific variables of the scenario to be controlled.

Forecasting models will allow to consider variables (climatic, social, economic, habit-related, etc.) that can influence the accuracy of forecasts ^[9]. These authors remark that energy demand estimates in disaggregated scenarios, such as residential users in smart buildings, are more complex compared to energy demand estimates for an aggregated scenario, such as a country. Disaggregating the demand also facilitates the implementation of demand response, as different prices can be offered based on the criteria set by the power company.

The gradual integration of intelligence at the transmission, distribution and end-user levels of the electricity system aims to optimize energy production and distribution to adjust producers’ supply to consumers’ demand. Moreover, smart grids seek to improve fault detection algorithms ^[16]. Accurate demand forecasts are very useful for energy suppliers and other stakeholders in the energy market ^[17]. In fact, load forecasting has been one of the main problems faced by the power industry since the introduction of electric power ^[18].

3. Important Factors in Demand Forecasting

Electricity demand is affected by different variables or determinants. These variables include forecasting horizons, the level of load aggregation, weather conditions (humidity, temperature, wind speed, and cloudiness), socio-economic factors (industrial development, population growth, cost of electricity, etc.), customer type (residential, commercial, and industrial), and customer factors in relation to electricity consumption (characteristics of the consumer’s electrical equipment) (e.g., ^[19]^[20]^[21]^[22]^[23]).

To fully understand demand forecasting techniques and objectives, it is necessary to examine these determinants. In this section, the authors will focus on (1) period, (2) economic issues, (3) weather conditions, and (4) customer-related factors.

3.1. Period or Forecasting Horizon

The period commonly referred as forecasting horizon is probably one of the factors that has the greatest impact.

According to different authors (e.g., ^[17]^[24]), demand forecasting can be classified into three categories with respect to the forecasting horizon:

Short-term (typically one hour to one week).
Medium-term (typically one week to one year).
Long-term (typically more than one year).

Factors affecting short-term demand forecasting usually do not last long, such as sudden changes of weather ^[22]. The quality of short-term demand forecasting is critical for electricity market players ^[20]. On the other hand, the influencing factors of medium-term demand forecasting often have a certain time duration, such as seasonal weather changes. Finally, the factors influencing long-term demand forecasting last for a long time, typically several forecast periods, e.g., changes in Gross Domestic Product (GDP) ^[22]. Indeed, economic factors have an important impact on long-term demand forecasting, but also on medium and short-term forecasting ^[25].

The authors of ^[26] identify the following categories in relation to the forecasting horizon:

Very short-term (typically seconds or minutes to several hours).
Short-term (typically hours to weeks).
Medium-term and long-term (typically months to years).

According to these authors, very short-term demand forecasting models are generally used to control the flow. Short-term demand forecasting models are commonly used to match supply and demand. And, finally, medium-term and long-term demand forecasting models are typically used to plan asset utilities.

The authors in ^[27] showed that the load curve of grid stations is periodic, not only in the daily load curve, but also in the weekly, monthly, seasonal, and annual load curves. This periodicity makes it possible to forecast the load quite effectively.

Demand also reflects the daily lifestyle of the consumer ^[28]. Consumers’ daily demand patterns are based on their daily activities, including work, leisure and sleep hours. In addition, there are other demand variations patterns over time. For example, during holidays and weekends, demand in industries and offices is significantly lower than during weekdays due to a drastic decrease in activity. Finally, power demand also varies cyclically depending on the time of the year, day of the week, and time of day ^[22].

3.2. Socio-Economic Factors

Socio-economic factors, including industrial development, GDP, and the cost of electricity, also significantly affect the evolution of demand. Indeed, as mentioned in the previous section, economic factors considerably affect long-term demand forecasts, and also have an important impact on medium- and short-term forecasts.

For example, industrial development will undoubtedly increase energy consumption. The same will be true for population growth. This means that there is a positive correlation between industrial development, or population growth, and energy consumption.

GDP is an indicator that captures a country’s economic output. Countries with a higher GDP generate a greater quantity of goods and services and will consequently have a higher standard of living and lifestyle habits, which will stimulate energy demand.

Another economic factor to consider is cost, as it also affects demand. For example, when the price of electricity decreases, wasteful electricity consumption tends to increase ^[22].

The cost of electricity depends on different factors and is shaped in different ways. For example, in some countries such as Spain, there are two markets (regulated and free) for electricity. In the free market, the cost of electricity is established in the contract signed by the consumer. In contrast, in the regulated market, the price of electricity depends on supply and demand. The price is updated hourly and fluctuates. From the demand side, the more electricity is demanded, the more expensive it is. When less electricity is demanded, the cheaper it is. Normally, it is cheap to use electricity at dawn and expensive to do it when everyone else is using it (e.g., at dinner time).

But it is not only the demand that influences prices, but also the supply of energy. The reason is that variations in the price of electricity on the regulated market are caused by differences between demand and supply. Consequently, supply must consider the different ways of generating electricity, which have different costs. The cheapest is electricity generated by renewable energies such as solar, wind and hydroelectric. The price of nuclear energy is also low; however, in many countries (e.g., Spain), nuclear energy does not cover all energy needs. Thermal (coal), cogeneration, or combined cycle—whose main fuel is gas—tends to be more expensive. It is also important to remember that the main sources of renewable energy, such as hydroelectric or wind, depend on uncontrollable external factors. For example, sufficient rainfall is essential to produce hydroelectric power. However, there is no way to control the weather to make it favorable for producing electrical energy. Given the above, the price is determined by the price of a mix of different sources of power generation, from cheapest to most expensive, until the entire energy demand is met.

3.3. Weather Condition

There are different weather variables relevant for demand forecasting such as temperature, humidity, and wind speed.

The influence of weather conditions on demand forecasting has attracted the interest of many researchers. As an example, the authors in ^[29] proposed different models to forecast next day’s aggregated load using Artificial Neural Networks (ANNs), considering the most relevant weather variables—more specifically, mean temperature, relative humidity, and aggregate solar radiation—to analyze the influence of weather.

Some authors have studied the relationship between temperature and electricity consumption and claim that the correlation between temperature and the electricity load curve is positive, especially in summer (e.g., ^[25]).

Currently, heat waves have become more common around the world, as well as the possibility of extreme temperatures. In addition, heat waves are not only more frequent, but also more intense and longer lasting. Moreover, the nights are getting warmer, which is an added problem. The main effect of a heat wave is an increase in energy consumption as the consumer turns on the air conditioning more and for longer periods of time. Additionally, cooling systems must work harder as they must cope with higher temperatures.

During the summer, heat waves force the grid to be at maximum capacity. In fact, one of the ways in which a heat wave affects consumption is through the increased saturation of the electrical grid. While cold waves are counteracted with electricity, gas, wood, etc., heat waves can only be fought with electricity. In other words, the devices that consumers use for cooling are mainly powered by electricity. For this reason, heat waves generate more stress on power lines, as well as higher consumption.

It should be noted that, in colder countries, the increase in consumption during a heat wave is usually lower. This is because the installation of air conditioning systems is not as common as in warmer countries. However, these colder countries are facing heat waves that did not occur in previous years (before climate change) and this is causing them all type of problems, as they are less prepared. This situation is forcing these countries to make changes such as increasing the use of cooling systems.

On the other side, experience of the harshness of temperature increases with humidity, especially during the rainy season and summer. For this reason, electricity consumption increases during humid summer days. It is also important to note that in coastal areas, such as the Mediterranean area in Spain, electricity consumption tends to be higher. This is both because houses tend to have more electrical equipment than in other areas, and because of the high degree of humidity due to the proximity of the sea.

Wind speed also affects electricity consumption. When it is windy, the human body feels that the temperature is much lower and more heating is needed, which increases electricity consumption. However, it should also be noted that wind energy is one of the main renewable energies. In other words, when there is wind, electricity consumption increases, but at the same time its price decreases. This is because, as explained in the previous section, the price of the electricity is usually determined as a mix of the different energy sources, from cheapest energies (renewables, including wind, and nuclear) to the most expensive generation sources (thermal, combined cycle).

Temperature, humidity, and wind affect the use of electricity. Humidity and temperature are also the main weather variables used in electricity demand prediction systems to minimize operating costs. However, other factors, such as clouds, also play a role. For example, during the day, when clouds disrupt sunlight there is usually a drop in temperature and, consequently, higher electricity consumption.

3.4. Customer Factors

The type of customer (residential, commercial, and industrial), as well as other customer factors related to electricity consumption (characteristics of the consumer’s electrical equipment) can also affect demand. This is important because most energy companies have different types of customers (residential, commercial, and industrial consumers), who have equipment that varies in type and size. These different types of customers have different load curves, although there are some similarities between commercial and industrial customers.

4. Classification of Demand Forecasting Techniques

This section classifies demand forecasting models according to three different criteria: (1) period, (2) forecasting objective, and (3) type of model used.

The first classification focuses on the point of view of the period to be forecasted, i.e., the forecasting horizon. To select this criterion, the electricity demand determinants presented in the previous section have been considered. The second classification focuses on the point of view of the forecasting objective, differentiating between forecasting techniques that produce a single value and those that produce multiple values. Finally, the third classification focuses on the point of view of the model used.

4.1. Classification of Demand Forecasting Techniques according to the Forecasting Horizon

As explained in the previous section, the main forecasting horizons that can be identified are the following:

Very short-term: typically from seconds or minutes to several hours.
Short-Term: typically from hours to weeks.
Medium-Term: typically from a week to a year.
Long-Term: typically more than a year.

The main difference is the scope of the variables used in each case. Very short-term forecasting models use recent inputs (typically minutes or hours), short-term forecasting models use inputs typically in the range of days, and medium and long-term forecasting models use inputs typically in the range of weeks or even months.

Power companies are particularly interested in producing accurate forecasts for the load profile (e.g., ^[9]^[30]^[31]). This is because it can directly affect the optimal scheduling of power generation units. However, due to the non-linear and stochastic behavior of consumers, the load profile is complex, and although research has been done in this area, accurate forecasting models are still needed ^[32].

4.2. Classification of Demand Forecasting Techniques by Forecasting Objective

Forecasting models can be also classified according to the number of values to be forecasted. In this case, two main categories can be considered.

The first category refers to forecasting techniques that produce only one value (e.g., next day’s total load, next day’s peak load, next hour’s load, etc.). Examples are found in ^[33]^[34].

The second category refers to forecasting techniques that produce multiple values, e.g., the next hours’ peak load plus another parameter (e.g., the aggregate load) or the load profile. Examples are found in ^[35]^[36]^[37].

Generally speaking, one-value forecasts are useful for optimizing the performance of load flows. On the other hand, multiple-value forecasts are mainly used for energy generation scheduling ^[9].

4.3. Classification of Demand Forecasting Techniques according to the Model Used

The model to be used is usually decided by the practitioner. In terms of models, the main groups are linear and non-linear approaches.

Linear models include Spectral Decomposition (SD), Partial Least-Square (PLS), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Conditional Heteroscedasticity (ARCH), Auto-Regressive (AR), Auto-Regressive and Moving Average (ARMA), Moving Average Model (MAM), Linear Regression (LR), and State-Space (SS).

Linear techniques have progressively lost importance and interest in favor of non-linear techniques based on ANNs. Deep Learning models use ANNs, inspired by the human nervous system. These models can learn patterns from the data generated and forecast peak demand in the context of today’s complex smart scenarios, where a large amount of data is continuously generated from different sources ^[7].

5. Fundamentals and Concepts of Machine Learning and Deep Learning Systems

Artificial Intelligence is a complex concept that, in a nutshell, refers to machine intelligence ^[38]. Unlike humans, Artificial Intelligence can identify patterns within a large amount of data using a quite limited amount of time and resources. Furthermore, the computational capacity of machines does not decrease with time and/or fatigue ^[39].

Artificial Intelligence systems use different type of learning methods, such as Machine Learning and Deep Learning.

5.1. Machine Learning

Machine Learning algorithms are pre-trained to produce an outcome when confronted with a never-before-seen dataset or situation ^[40]. However, the computer needs more examples to learn than humans do ^[41]. Machine Learning allows the introduction of intelligent decision-making in many areas and applications where developing algorithms would be complex and excellent results are needed ^[42].

There are different categories of Machine Learning algorithms including supervised, semisupervised, unsupervised, and reinforcement learning.

5.2. Deep Learning

Machine Learning can be classified into shallow and deep, considering the complexity and structure of the algorithm ^[41]. Deep Learning uses multiple layers of neurons composed of complex structures to model high-level data abstractions ^[43]. The type of output and the characteristics of the data determine the algorithm to be used for a particular use case ^[44].

Deep Learning uses ANNs inspired by the human nervous system ^[45]. This type of network typically has two layers of input and output nodes respectively, connected to each other by one or more layers of hidden nodes. Possible deep ANN architectures include Multilayer Perceptron (MLP), Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Generative Adversarial Network (GAN), and Convolutional Neural Network (CNN or ConvNet).

According to the literature review, the most widely used models in the energy domain are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Deep Q-Networks (DQNs) and Conditional Restricted Boltzmann Machine (CRBM) and a variation of any of them, a combination of two or more of them, or the combination of any of them with other techniques.

6. Deep Learning Models and Demand Forecasting in the Context of Smart Grids

Researchers have proposed forecasting models in the two main areas where Deep Learning techniques can be applied ^[8]: (1) demand management (e.g., ^[46]^[47]) and (2) grid control (e.g., ^[48]^[49]^[50]).

Due to the growing demand for energy from different sectors, supply and demand must be balanced in the electrical grid. In this scenario, smart grids can play an important role by providing a bidirectional flow of energy between consumers and utilities. Unlike traditional electrical grids, smart grids have sophisticated sensing devices that generate data from which energy patterns can be derived. These patterns are extremely useful for load forecasting, peak shaving, and demand response management.

As the amount of data generated by a smart grid is huge and constantly increasing, Deep Learning based models are a good option to understand consumption patterns and make forecasts. Researchers have studied the possibilities of using Deep Learning models, with LSTM networks playing a leading role (e.g., ^[32]^[51]^[52]).

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