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This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting. [ABSTRACT FROM AUTHOR]

Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Short‐term forecasting of water demand is a crucial process for managing efficiently water supply systems. This paper proposes to develop a novel graph convolutional recurrent neural network (GCRNN) to predict time series of water demand related to some water supply systems or district metering areas that belong to the same geographical area. The aim is to build a graph‐based model able to capture the dependence among the different water demand time series both in spatial and in temporal terms. This model is built on a set of different graphs, and its performance is compared to two methods, including a state‐of‐the‐art deep long short‐term memory (LSTM) neural network and a traditional seasonal autoregressive moving average model. Additionally, the forecasting model is tested in a condition when a sensor has a malfunction. The results show the ability of the GCRNN to produce accurate and reliable forecasting, especially when based on graph built while accounting for both time‐series correlation and spatial criteria. The GCRNN consistently outperforms the LSTM during the fault test, showing its ability to generate a robust prediction for days after a sensor malfunction, given the GCRNN's ability to benefit from the other time series of the graph. Key Points: Novel and innovative methodology based on graph convolutional recurrent neural network for short‐term water demand forecastingThe proposed approach provides a reliable prediction also during sensor's malfunction events, enabling new possible usage for this methodMultiple real‐time series of water demand as a case‐study [ABSTRACT FROM AUTHOR]

Our research examines how to integrate human judgment and statistical algorithms for demand planning in an increasingly data‐driven and automated environment. We use a laboratory experiment combined with a field study to compare existing integration methods with a novel approach: Human‐Guided Learning. This new method allows the algorithm to use human judgment to train a model using an iterative linear weighting of human judgment and model predictions. Human‐Guided Learning is more accurate vis‐à‐vis the established integration methods of Judgmental Adjustment, Quantitative Correction of Human Judgment, Forecast Combination, and Judgment as a Model Input. Human‐Guided Learning performs similarly to Integrative Judgment Learning, but under certain circumstances, Human‐Guided Learning can be more accurate. Our studies demonstrate that the benefit of human judgment for demand planning processes depends on the integration method. Highlights: Human judgment is an essential element of demand forecasting but requires integration into the forecasting process.Giving people too much influence may introduce more noise than signal. Our research examines different ways of integrating human judgment into a forecasting process and shows that an effortless way of doing so‐‐by allowing people to indicate that a special event is affecting a forecast and an algorithm to estimate the impact of that event‐‐performs remarkably well in comparison to other methods. [ABSTRACT FROM AUTHOR]

Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely used to support demand management decisions, but these strategies require large data sets to achieve an accurate electric consumption forecast, so they are not commonly used for buildings with a short history of record keeping. Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast. The proposed forecasting strategies were evaluated with Random Forest, eXtreme Gradient Boost, Convolutional Neural Network, and Temporal Convolutional Network algorithms using 4 years of electricity consumption data from two buildings located on the campus of the University of Valladolid. For performance evaluation, two scenarios were proposed for each of the proposed forecasting strategies. The results showed that for forecasting horizons of 1 week, it was possible to obtain a mean absolute percentage error (MAPE) below 7% for Building 1 and a MAPE below 10% for Building 2 with 6 months of data, while for a forecast horizon of 1 month, it was possible to obtain a MAPE below 10% for Building 1 and below 11% for Building 2 with 10 months of data. However, if the distribution of the data captured in the buildings does not undergo sudden changes, the decision tree algorithms obtain better results. However, if there are sudden changes, deep learning algorithms are a better choice. [ABSTRACT FROM AUTHOR]

The increase in electricity demand requires improved energy planning programs, which involve better energy distribution. Therefore, precise energy demand forecasting is of great importance for optimizing energy distribution. In this respect, a new hybrid forecasting model was proposed in this study for multi‐hour forecasting of total energy requirement (cooling and heating load of buildings) in three different regions in Algeria with different climate conditions. The proposed models are based on two main steps: In the first step, time‐series data were decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) into a different intrinsic function (IMFs), and then extreme learning machine (ELM) is employed as essence predictors. The proposed CEEMDAN‐ELM model uses the decomposed IMFs as input data, and the total energy concern as a desired output. The integrated CEEMDAN‐ELM model was evaluated and validated on three different databases each of which had 2 years of measurement on an hourly scale. Experimental results show that the hybridization mechanism CEEMDAN‐ELM outperform the stand‐alone model (ELM) in terms of forecasting errors over the entire forecasting horizon. Forecasting results of CEEMDAN‐ELM led to a normalized root mean square error (nRMSE) in the range of [0.71–4.66] for all studied regions and horizons whereas conventional ELM provides an nRMSE in the range of [0.93–16.89]. [ABSTRACT FROM AUTHOR]