Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England

Nowadays, forecasting methods have gained significant attention, particularly with the design and development of energy systems. In fact, accurate load and price forecasting is crucial for effective planning, controlling, and operation of power systems, especially with renewable energy sources (RES). This paper implemented an improved Markov Chain Artificial Neural network (ANN-MC) for load forecasting. The proposed design involved a two-step implementation process, considering various statistical factors such as daily and weekly load, date/time of the year, environmental factors (e.g., dry bulb temperature and dew point), and user behaviour on weekdays and weekends. The test cases were conducted using historical data from ISO New England spanning the years 2004 to 2020. Moreover, the validation of the proposed model has been confirmed through comparing the results with those of Gaussian Process Regression (GPR), Regression Decision Tree (RDT), deep learning Bi-Long Short Memory (bi-LSTM), MLP, and conventional ANN. This article discusses the use of various performance indices such as MAPE, MPE, skewness, kurtosis, and risk indices for evaluating model performance. The performance of a developed model is compared with a conventional ANN model, and its performance is studied for both yearly and seasonal variations. In addition to existing indices, the article proposes two risk indices. The first is based on evaluating the standard deviation of load increment for each time, while the second is based on MC-ANN, the error between forecasted and actual loads. The risk assessment is compared between different cases such as actual load, load forecasting with ANN, and enhanced ANN-MC. Finally, the result confirms that the enhanced ANN-MC provides a higher yearly MPE value compared to other methods. In addition, it has a higher computational time than the conventional ANN-MC model, which is approximately 180.7s and 221.8s, respectively.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).