Green Energy AI Taxonomy

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This entry is adapted from the peer-reviewed paper 10.3390/en16155718

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI.

green AI services load forecasting price forecasting energy usage load profiling smart-grid

1. Introduction

An emerging solution to the problem of power-hungry algorithms is Green AI or making AI development more sustainable. By reducing the energy consumption of data centers and other AI infrastructure, Green AI can help reduce the hidden costs of technology development. Green AI is leading the way to creating a more sustainable and responsible form of technology development, allowing us to achieve more accurate results with less energy consumption. Green AI can also be more cost-effective and efficient, in addition to being more environmentally friendly. For instance, a data center powered by renewable energy can save money on energy costs. As a concept in AI, Green AI refers to research that achieves novel results without increasing computational costs. This discussion focuses on Green AI services. The services include green energy power generation, load forecasting, load profiling, demand and response, electricity price, storage assessment, outage, and power consumption. All these services motivate using renewable energy sources in various service sectors. There has been various research conducted under green energy services. The paper ^[1]^[2] focuses on solar power generation and its uses. Paper ^[3] studies power generation via wind energy. There are various parameters considered for load forecasting, like in ECM (energy cloud management) for distribution of power and support operation ^[4]; load prediction is made at various levels and different time durations like short, mid, and long terms ^[5]^[6], considering parameters like temperature, humidity, and climatic changes ^[5]^[6]^[7]^[8]. While in ^[9] researchers study the load forecasting for solar power and wind energy. The state-of-the-art technique is reviewed and used for renewable energy load forecasting ^[10]. One of the major roles in load forecasting is played via load profiling assessment ^[11]. It shows the variation in electrical load over time.

The energy is generated and priced based on demand and response from the market ^[12]^[13]. A large set of fundamental drives like system loads and technical indicators are used for spot price prediction ^[14]^[15] based on a long review of data-driven models for around a decade ^[16]. The prices are raised or dropped based on their power consumption and usage analysis. There are various machine-learning models and techniques discussed herein. The analysis ^[17] discussed fundamental data-driven methods that are used to analyze the structure of energy consumption ^[18], a derived hybrid machine learning model, and ^[19] state-of-the-art models. All these services focus on predicting and analyzing various parameters of Green AI services.

As Green AI becomes a widely accepted measure of research quality in addition to accuracy, researchers can develop models that are environmentally friendly and inclusive of all communities. To utilize technology to its full potential, we need to apply various machine learning techniques along with deep learning algorithms and forecasting techniques to guarantee the accuracy of the models. Generally, there are three types: Data-driven, Classical, and Artificially intelligent. A classical classifier, such as a SARIMA or ARIMA (autoregressive integrated moving average), can also be built with artificial intelligence models, such as CNNs, LSTMs, or FNNs. In addition to ensuring efficient electricity usage, these forecasting models are designed to predict electricity costs.

2. Green Energy AI Taxonomy

In recent years, there has been an increased demand for environmentally friendly and sustainable solutions, and communities are looking for ways to reduce their carbon footprints. Green AI services can enable this transition by optimizing energy usage, reducing waste, and promoting renewable resource usage. The use of artificial intelligence services like Green Energy Power Generation, Green Energy Load Forecasting, Load Profiling Assessment, Demand Response Analysis, Electricity Price Forecasting, Green Energy Storage Assessment, Electricity Outage Analysis, and Power Consumption/Usage Prediction can enable communities to make informed decisions about energy usage and resource allocation through data-driven insights and tools. All the mentioned services are targeted to help communities achieve the ultimate goal of using energy efficiently, which would reduce the impact on the environment would lead to the sustainable development of the community.

The taxonomy of the green energy cloud AI service is presented in Figure 1, which is given below in detail. The table shows the commonly used machine learning models that will be explained in detail later. This model uses various input parameters and has different complexity levels, and usage varies based on the time frames that are considered for the researchers.

Figure 1. Taxonomy of green energy cloud AI service.

Table 1 lists the services and ML models commonly used. The models can be used to predict customer behavior, detect anomalies, and perform data analysis. The services help to deploy models quickly and scale the machine learning applications easily. They also help to manage the models and monitor their performance.

Table 1. Green AI Services.

Green AI Services	Services Descriptions	Commonly Used Machine Learning Models
Green Energy Power Generation	The most common green energy generation is (1) Wind power, (2) Solar Energy, (3) Tidal power using turbines, and (4) Geothermal Energy. Wind energy generated by a gas flow can generate electricity in windy areas or where the terrain differs. Tidal energy is used to power generators. However, the water movement is triggered by changing tides, so this type of energy generation is categorized as hydropower. The world’s largest natural resource is geothermal energy. Unlike other green energy generation methods, geothermal energy is less affected by environmental and climate factors. Using sunlight to generate electricity, solar energy can be used to power developing regions where sunshine is available for the bulk of the day. Aside from this, the power generation period is sufficient to meet human needs for electricity. The duck curve shows that in locations with substantial amounts of solar and wind power capacity, power production peaks in the mid-evening hours when solar power is no longer available.	LR, RPR, DTR, SVR, RFR, LSTM, MLP, LR, RF, ANN, SVM, CNN, GRU, RBM, RNN, SAE, VAE, BPNN, DNN, ARX
Green Energy Load Forecasting	There are three types of load forecasting: short, mid, and long-term. Regression models, time series forecasting, artificial neural networks, statistics, and fuzzy logic are mostly used for short-term forecasting. Along with the above algorithms, the end-use and econometric algorithm approaches are for long and mid-term forecasting. A similar-day approach, like weekends and holidays, is applied for mid and long-term forecasting.	LSTM, ANN, SVR, MLR, MLP, SVM, LR, IFOA-w-LSSVM, RF, FNT
Load Profiling Assessment	Load curves or load profiles in power systems show the variation in electrical demand or load over time. This information helps generation companies determine how much power they will need at any given moment. Energy load profiles show how much electricity one’s customers use daily and/or seasonally. It will show the variation in a customer’s electrical load over time.	LSTM, ANN, SVR, MLR, MLP, SVM, LR, IFOA-w-LSSVM, RF, FNT
Demand Response Analysis	Due to supply constraints, the DR (demand response) process can reduce energy load during peak demand periods. Demand response involves adjusting or reducing electricity demand to maintain system balance. A few electric system planners and operators wisely calculate the demand and provide the supply; this helps in maintaining balance. This leads to the price reduction of a wholesale market, lowering the retail rates. This can benefit the customers through time-based, critical peak, and variable peak pricing. The prices will be real-time prices that will engage the customers in rebates for peak usage and demand response efforts.
Electricity Price Forecasting (EPF)	The EPF is a division of electricity that helps forecast wholesale electricity prices, both forward and spot. Electricity prices are forecasted using statistics (econometrics, technical analysis) techniques based on previous prices and exogenic factors. Typically, prices are forecast using consumption and production figures, as well as weather variables.	MLP, ANN, ARX, ARMAX, S-ARIMA, MNN
Green Energy Storage Assessment	The assessment avoids the imbalances between energy demand and production. This process captures energy at a time and uses it for the future. This process captures energy each time and uses it for the future. It is generally known as a battery or accumulator when it is used to store energy.	LSTM, RNN, Physics Constrained NN, MEA-BP Algo., GRU, CNN, ED, LSTM, RF, MLP, LR, PLS Reg., SVM, RVM, GPR, Gradient Boosting
Electricity Outage Analysis	It is simply a power failure that leads to electrical power supply interruption.
Power Consumption/Usage Prediction	Forecasting energy consumption is a time series regression problem. The method involves predicting a customer’s energy consumption based on their history. Various machine learning algorithms have demonstrated promising results along with time series and regression.	SVR, ANN, RF, DT, SVM

Green energy power generation—The electricity generated by renewable energy sources is called green energy, and the process of converting green energy into electricity is called green energy power generation. The methods like pumped storage are used before the electricity is transmitted or delivered to the end user. Green energy power generation involves wind, solar, geothermal, and tidal. There is research that explains the features, application, and future scope that would enhance the forecasting methods, which would lead to higher accuracy. Akhter et al. ^[1] discuss methods for forecasting PV (photovoltaic) power output using metaheuristics and machine learning methods, focusing on solar power generation, considering factors like (1) temperature, (2) sunrays, (3) humidity, and (4) atmospheric pressure. This uses the PV output of the power forecast based on time horizons. This study uses MCP (measure-correlate-predict) models for forecasting power generation and alternative methods such as neural networks or hybrid models. In a study by Utpal et al. ^[2]. Considering wind speed and meteorological variables, Soraida et al. ^[3] provided an overview of wind electricity generation. The study focuses on medium- and long-term trends in wind speed and power, which can be considered in summative planning. At the same time, short-term forecasts are used mainly for operational purposes.

They examined machine learning models for solar energy generation based on accuracy, reliability, the model’s computational cost, and the model’s complexity. The importance of the correlation among input data, output data, and preprocessing of model discussion is provided for various forecasting time frames. Along with PV-assembled smart buildings, well-organized managed systems, EV (electric vehicle) charging, and smart grids are covered.

Green energy storage assessment—This is the storage of electricity on a larger scale using various collection methods of a power grid. This assessment generally considers when the power is in abundant form and is cheap, mainly from renewable sources, or while demand is less, and we know that demand will rise and prices will be higher soon. The evaluation of storage analysis includes analysis and prediction mainly of the SoC (state of charge), lifespan, and SoH (state of health).

Green energy load forecasting—This is the part that studies the consumption of electricity by the electric circuits and appliances in the community ^[4]. Load management is required, and ECM (Energy Conservation Measures) is used for the efficient distribution, supporting operations, and planning of various processes for managing electrical load. The pricing of retails and dynamic powers is based on the consumption and prediction of electricity. The accuracy of consumption is significant at lower ends compared to a higher level ^[5]. In the term load forecasting, the “load” refers to the demand, which is measured in kilowatts (kW), or in energy which is measured in kWh (kilowatt-hours). The hourly data uses the same power and energy magnitude, so no distinction is made between demand and energy. Load forecasting involves the prediction of magnitudes and geographic localities accurately for a planning horizon divided into different periods. Generally, the hourly total system load is in the basic quantity of interest. However, load forecasting also considers values of peaks and load hourly, day-wise, weekly, and month-wise ^[6]. Green energy load forecasting includes the motor, industry equipment, fridge, air conditioner, lighting, water heater, and elevator. Intensive research is conducted on load forecasting, considering a variety of parameters for a more accurate evaluation, leading to greater use of renewable energy, and promoting Green AI.

According to the paper ^[7], various electricity load forecasting models were presented by Yildiz et al. 2017 that focus on regression models. Considering all the seasons and various climatic changes and conditions, day-ahead hourly loads are predicted using the data obtained from buildings and campuses. The review concluded that using the regression model is not just limited to forecasting the total energy load of buildings, but also includes HVAC (heating, venting, and air conditioning) loads and retrofit savings. The forecasting methods for wind power, solar, and load are reviewed by H. Wang et al. There has been a study of wind-speed/irradiance corrections adopted by NWP (numerical weather prediction), as well as wind and solar power forecasting methods, and load forecasting methods used by the NWP for demand forecasting, as well as wind and solar power forecasting methods for demand forecasting. The paper describes the NWP as one of the most critical and important factors that would affect the accuracy of forecasts by wind and solar, mainly for short-time forecasting. The paper also focuses on the challenges and future research direction of solar, load, and wind ^[9]. The review ^[8] describes the use of machine learning in forecasting loads in district heating and cooling systems. A study was conducted on the prediction of heating loads and demands as well as on the design, maintenance, and scheduling of heating systems. The review considers parameters such as weather/climate changes, socioeconomic features, historic energy consumptions, and thermal load for predicting both the short and long-time load. The paper also discusses using ML-based algorithms to link DHC to smart electricity grids. Sheraz et al. conducted a comprehensive review of the renewable energy and load forecasting literature using deep learning models and state-of-the-art techniques at residential and commercial sectors ^[10]. It provides an overview and categorization of deep learning models used in smart energy management systems. Models of forecasting are evaluated according to the types of energy (wind and solar), building types (commercial and noncommercial), and temporal granularities (five minutes, ten minutes, fifteen minutes, thirty minutes).

Load profiling assessment—The load profile shows the graph of the variation between electrical load v/s time. The profiling can change or vary based on the type of customer. The customer type includes residential customers, commercial plots, and industrial areas. Temperature and holiday seasons also affect the load profile assessment graphs. During electricity generation, the producers use the information to plan the need for electricity and make it available at a given time ^[11]. The load profile includes consumer learning, training, classifying, predicting, consumer load pattern, consumer energy consumption behavior, and peak time.

Demand response analysis—It is defined as “Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” ^[12]. One of the most critical tasks in ECM is DRM (demand response management). The DRM helps balance the gap between the demand and supply of the energies. Using the DRM system, we can shave off the valley or peak in demand for electric power in real-time, which benefits the companies and consumers in electricity bills and real-time pricing schemes, balancing the loads and increasing user involvement ^[20]. According to the paper, Jebaraj et al. discussed energy modeling issues dealing with different forms of energy like solar, wind, biomass, and bioenergy.

Additionally, they reviewed and presented various models, including energy planning, demand-supply, and forecasting models. Along with it, they also reviewed various optimized models, renewable energy models, and models that help in reducing emissions ^[20]. The paper focuses on problems like the demand and supply of energies and weather impact on the models. It focuses on the optimized cost, multi-level optimization: self-sufficiency, conservation, and sustainability, and models that use AI and show symbolic reasoning, flexibility, and explanation capabilities. Suganthi et al. reviewed the energy forecasting models demand management, and econometric models for planning future needs, identifying conservation measures, framing policy decisions, optimizing energy utilization, and reducing emissions. The review states that researchers can utilize sophisticated modeling techniques, including gray prediction, genetic algorithms, fuzzy logic, SVR (support vector regression), ACO (ant colony optimization), and PSO (particle swarm optimization) for macroeconomic planning to predict energy demand accurately ^[14].

Electricity price forecasting—The price of electricity is based on the seasonality at a daily level, weekly level, and to a certain extent, an annual level. The majority of studies conducted on the EPF, along with various short-term time horizons, mainly focus on day-to-day markets. Mid-term is preferred for managing risk, balance sheet calculations, and derivative pricing. In most cases, the evaluation is conducted on the distribution of prices based on future periods rather than actual point forecasts. This type of modeling technique is mainly used in long-standing traditions in finance, and the inflow of finance solutions is measured. Long-term forecasting is mainly based on investments, and profitability analysis is conducted. Based on long-term forecasting, planning and determining future sites or new sources of fuel power plants are considered. Similarly, spot pricing approaches include statistical methods like econometric and technical analysis, multiagent factors, and CI (computational intelligence) models. The spot price is dependent on fundamental drivers that include demands and consumption figures, weather parameters, cost of fuels, and reserve margin; in other words, surplus generation is available generation minus/over predicted demand, an important power grid component or market is scheduled for maintenance or forced to go down ^[15]^[21].

There is dynamic forecasting, along with the time parameter used for forecasting. Dynamic forecasting prices use techniques like time of use pricing, pricing in real-time, critical peak time pricing, and inclined block rate. A fine-resolved prediction of electricity market prices is especially important in electricity sectors with a high percentage of non-dispatchable renewable energy. Demir et al. reviewed the electricity price based on technical indicators that can help improve the accuracy of forecasts for day-ahead electricity markets. This paper shows that traders’ behavioral biases can be captured by TIs, resulting in statistically significant reductions in forecast error with ML models ^[16]. Lu et al. used a decade-long review that considered input parameters like natural gas and crude oil, carbon prices, and electricity prices as input parameters for data-driven models. The paper covers the aspects of accuracy, time horizon, and input variables for energy price prediction ^[17].

Power Consumption/Usage Analysis—The article by Yixuan et al. discussed prevailing data-driven methodologies used to evaluate building community energy consumption across granularities and archetypes, including machine learning methods for predictions and clustering for classification. The paper addresses several building-related applications using data-driven methodologies that assist the problems of loads, energy patterns, energy consumption based on regions, building stock benchmarking, and retrofit strategies on global levels ^[18]. The paper explains applications of the state-of-art technique in machine learning models for energy systems explained by Mosavi et al. They are used for energy systems, applications, and taxonomy, contributing to efficient energy use and handling the energy grid’s sustainability. A hybrid machine learning model for energy systems is presented in the paper to help identify ML modeling techniques and energy types and to understand how accurate the model performs, the robustness of the model, its precision, and its generalization abilities. Extensive reviews of ML models in diverse application areas have demonstrated the popularity and effectiveness of ML models in almost any energy domain ^[19]. The purpose of the study is to review recent studies related to designing energy models and estimating energy consumption in various areas. Using novel hybrid and ensemble prediction technologies, the research reports a significant increase in accuracy and performance using the state-of-art energy consumption model ^[22].

Along with all the above Green AI services, the work on the concept of VPP (virtual power plant) and duck curve plays an important role in green energy generation, storage, and load forecasting, it contributes to the aspects of energy saving, economic aspect, power transactions, communications between technologies, and resource allocations. Additionally, it enhances the power plant’s or consumer’s readiness and balances the performance energy range and storage ^[13]. There is vast research on this topic. The concept is explained using applications and challenges using the case studies of Europe, the USA, and Australia. The challenges in these regions using VPP were mainly resource allocation, communication between the technologies, power transactions, operations, and control systems ^[23]. The calculation and power flow of VPP of low and medium voltage in distributed networks are presented in the article. It uses the concept of DER (distributed energy resources) to see its impact on when capacity is at its maximum with storage units. The paper describes data acquired for a single VPP instance, and it is not easy to generalize these findings to other VPP locations ^[24]. The researchers in the paper have attempted to address the scheduling techniques, technical and economic aspects, and optimization of VPP. The paper concludes by suggesting that deep reinforcement learning handles feature extraction and scalability, as they are generated by combining reinforcement learning and deep learning ^[25]. The paper focuses on the economic aspect of VPP. It analyzes the possibility of combining the DERs and ESS in VPP and considers them as a single plant, this will influence the factors like price and production ^[26]. The study of VPP in Ireland, Belgium, and the Netherlands was conducted at the community level, where community energy meets intelligent grids. The three cVPPs had to abide by the existing energy system, making it challenging for them to play the chosen roles in the energy system, work on a local scale, and maintain their own needs and values, as explained in the paper ^[27]. This paper presents a comprehensive, integrated techno-economic modeling approach for an urban VPP that fully exploits the DER’s aggregated flexibility and results in attractive business cases ^[28]. A VPP model was developed to maximize operating profit to meet demand. It was applied to real data from an irrigation system comprising several Aragon (Spain) water pumping stations. It includes a wind farm, six hydroelectric power plants injecting the generated electricity directly into the distribution network, and on-site photovoltaic plants prioritizing self-consumption ^[29]. The paper proposes a multi-time scale stochastic optimization scheduling strategy for a new energy virtual power plant based on a robust stochastic optimization theory. The results show that the models can improve operating profit and new energy consumption capacity ^[30]. Based on the SARIMA-KF(Kalman-filter) hybrid algorithm for real-time optimal operation of VPPs, an adaptive and predictive energy management strategy for energy storage with renewable energy is proposed ^[31]. The proposed VPP mitigates issues related to DER penetration by providing grid frequency and voltage support, load forecasting, and power flow control. The input parameters considered for the DER issue are historical load and weather data, weather forecast inputs, and models of VPP players ^[32]. In a way, there are a lot of benefits to users, suppliers, and stakeholders using VPP. It supports the Green AI services by supporting the weather and load forecast management, real-time processing of historical data, giving alerts during market fluctuations during the day, and providing sufficient resources to smart grids at the time of peak hours. Currently, Tesla introduced Powerwall in CA, USA in the year 2015 and plans to launch its own virtual power plant in California. Swell Energy is also on the domestic scene with VPP designs ready to power homes throughout California, Hawaii, and New York City.

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1. Introduction

2. Green Energy AI Taxonomy

References