Electricity Demand Forecasting Using Machine Learning

Electricity demand predicting is essential for grid operations by making sure that stability between electricity supply and demand. We utilize machine learning (ML) for this work which leads to more accurate and adjusted predictions frameworks compared to existing time series models.

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The following is a process guide to deploy ML for electricity demand forecasting:

  1. Data Collection:
  • We gather previous electricity demand data more often basically in hourly intervals.
  • Identical external variables like weather data (temperature, humidity), calendar variables (holidays, day of the week), and other relevant activities can assist us.
  1. Data Pre-processing:
  • We manage missing values by imputation and elimination techniques.
  • Normalization and standardization the data when we need.
  • Feature engineering: Retrieving features such as hour of the day, month, weekday/weekend, etc. Lagged values (for instance: demand from past hours) also support us in our work.
  1. Time Series Split:
  • We utilize a time-based segment for instructing and test sets. For example, when we get data from 2010 to 2020, we train on 2010-2018 and test on 2019-2020.
  • For model validation we consider using a rolling forecast origin and time series in cross-validation.
  1. Selecting Model: There are several famous frameworks can be used for forecasting which we includes:
  • Linear regression models with time features and exogenous variables.
  • Random forests and gradient boosting machines
  • For series like time sequence data we employ Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks which give effectiveness.
  • To perform well for datasets with strong seasonal formats we use Prophet by Facebook.
  1. Model training:
  • We instruct our framework on the training dataset.
  • For improving the hyperparameters we implement cross0validation on the training set.
  1. Evaluating Model:
  • Validating the framework’s effectiveness on the test set. We utilize some general metrics for time series forecasting including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
  • To make sure there is no design left which would show our model is losing some structure in the data we examine the residuals’ plots.
  1. Deployment of Model: Once we get fulfilled with our model’s efficiency we apply it for real-time and batch predictions. We make sure that our techniques used to retrain the framework regularly with latest data.
  2. Review Loop: We constantly track the model’s forecasting and differentiate them with the real demand. This supports us in finding when the model begins to drift and requires retraining.
  3. Retraining: When the model’s efficiency gets worse we retrain it. This process includes extending new features, updating hyperparameters, and sometime selecting the latest framework.


  • Implementing domain skills assist us to enhance our framework. For instance, we knowing peak demand times, interpreting the significant of particular activities and analyzing the relationship between temperature and demand are useful in our project.
  • We group techniques where multiple frameworks detections are integrated and lead to more powerful and accurate predictions.
  • The deep learning structures like LSTMs can be very robust that they need more data and difficult to instruct and understand than earlier ML models.

     Finally, our choice of model and features hugely based on the state of data, the predicting horizon (short-term vs. long-term), and the particular needs of the electricity grid operation in our problem. So if you want more support contact our team and make you research work simply extraordinary by giving professional touch.

Electricity Demand Forecasting Using Machine Learning Thesis Ideas

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Electricity Demand Forecasting Using Machine Learning Ideas
  1. Electricity Demand Forecasting In Kerala Using Machine Learning Models


Electricity Demand Forecast, Machine Learning, Prediction, Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), K nearest Neighbors (KNN), XGBoost, Artificial Neural Network (ANN)

            An electricity demand prediction framework is constructed in our article to forecast the electricity demand. We employed various ML approaches like Linear Regression, Decision Tree, Random Forest, Support Vector Regression, K-Nearest Neighbors, XGBoost, and Artificial Neural Network to analyze the efficiency of these methods. We compared these methods in terms of several metrics. As a result, Random Forest provides greater outcomes than others.

       2.Integrating Weather Patterns into Machine Learning Models for Improved Electricity Demand Forecasting in Sri Lanka


Electricity demand, weather patterns, forecasting

            A prediction of monthly electricity demand in terms of historical data and weather patterns is the major goal of our research. We predicted the monthly electricity demand by utilizing Vector Auto Regression (VAR) and Long Short-Term Memory (LSTM) techniques. From the analysis, we described that, the VAR technique provides lower scores in evaluation metrics such as RMSE, MSE etc. Therefore, VAR technique is considered as an optimal method.

  1. Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms


Medium neural networks, whale optimization algorithm, support vector machine, error metrics, multi regression equations, Turkey

A combined model of MNN, WAO, and SVM is proposed in our paper to forecast the electricity demand. We carried out comparative analysis to evaluate the efficiency of these techniques by considering several statistical error metrics. We showed the connection among actual data and evaluated values and connections among dependent and independent attributes by using correlation matrix. As a consequence, MNN technique achieved better performance.

       4.Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine


National electricity demand, ARIMA, LSTM

            An innovative integrated technique for prediction of national demand electricity is suggested in our study by utilizing statistics and ML methods. We utilized macroeconomic regression to examine long-term yearly trend. We illustrated error term by the integrated ARIMA and LSTM “black-box” related model. We analyzed hourly session by using calendar regressors and multiple ARIMA technique. In that, an integration of multiple regression framework and LSTM hybrid framework is considered as an efficient framework for residual forecasting.

         5.A Comparative Analysis of Supervised Machine Learning Algorithms for Electricity Demand Forecasting


Big data, random forest regression

            A medium term electricity demand forecasting is carried out in our study by examined supervised machine learning approaches. We compared the performance of various methods including LR, MPR, SVR, ENR, GBR, DTR, RFR, and KNNR by considering different metrics. Results show that, non-parametric ML approaches provides greater efficiency when compared to parametric approaches.

      6.Performance Comparison of Simple Regression, Random Forest and XGBoost Algorithms for Forecasting Electricity Demand


Electricity Consumption, Regression Techniques, Supervised Learning

            By utilizing supervised learning based method named Linear Regression and ML based methods such as Random Forest and XGBoost; we developed a system for electricity utilization prediction in our research. In an hourly basis, by considering meteorological attributes and public holidays, we predicted the short-term consumption load.  We also evaluated the efficiency of utilized methods in prediction process.

  1. Forecasting household electricity demand with hybrid machine learning-based methods: Effects of residents’ psychological preferences and calendar variables


Household electricity demand forecasting, Residents’ psychological preferences, Calendar variables, Feature selection

In our study, a preprocessing of data with stationarity of time series is developed to handle the irregular trend factors. We selected important features by utilizing integrated method of RF and autocorrelation analysis. We employed various ML methods like KNN, SVR, RF and MLP to develop an integrated household electricity demand (HED) prediction framework. We aimed to add residents’ psychological preferences and calendar attributes and examined their impacts on HED. 

  1. Forecast electricity demand in commercial building with machine learning models to enable demand response programs


Deep neural network, Model assessment, Short-term load forecasting, Long Short-term Memory Networks, Demand response

            The common approaches of short-term load prediction are described in our article. We illustrated the fundamentals of LSTMs and SVM methods. We also carried out procedures like preprocessing of data and feature selection. We employed LSTMs and SVM methods for one-hour ahead load prediction and one-day ahead peak and valley load prediction.  When there is an adequate load data, LSTM method provides greater efficiency and in the case of inadequate load data, SVM method offers effective performance.

  1. User Behavior Analytics with Machine Learning for Household Electricity Demand Forecasting


User behavior analysis, ensemble learning

            For efficient short-term load forecasting (STLF), our article examines about the smart sensor data that suggests user behavior may be applied to ML based methods. Here we considered several input factors like external attributes and sensor data in some perspectives. We utilized decision tree related ensemble learning technique to build various STLF systems for every input factor. From the analysis, the system trained with only external attributes achieved greater results.

        10.Electricity demand forecasting in industrial and residential facilities using ensemble machine learning


Energy, artificial intelligence

            Ensemble ML techniques are employed in our research to construct an electricity demand prediction framework. We carried out various phases of preprocessing like handling of missing values, eliminating outliers, and standardization. To enhance accuracy and to minimize training time and overfitting issues, we extracted the features.  We utilized grid search technique to optimize the appropriate framework. As a result, Extra Trees Regressor based day ahead framework is considered as an efficient framework in terms of various metrics.


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