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Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

In recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach to address electrical load forecasting at various levels of disaggregation in the Peruvian Interconnected Electrical System (SEIN). The novelty of this approach includes utilizing meteorological data for training, employing an adaptable methodology with easily modifiable internal parameters, achieving low computational cost, and demonstrating high performance in terms of MAPE. The methodology combines modified Fuzzy ARTMAP Neural Network (FAMM) and hybrid Support Vector Machine FAMM (SVMFAMM) methods in a parallel process, using data decomposition through the Wavelet filter db20. Experimental results show that the proposed approach outperforms state-of-the-art models in predicting accuracy across different time intervals.

This chapter provides an indication of whether dynamic pricing could be competitive as a product on the Danish retail electricity market taking into account the possibility of consumers to respond to hourly prices in order to generate benefits. It estimates potential benefits of consumers switching to a dynamic pricing scheme under different assumptions of their ability to respond to prices. To gain insight into the distribution of benefits among households, the chapter uses disaggregated load profiles for different types of homes while taking into account all elements of the retail price including taxation. It also determines the gains of converting fixed per‐unit adders to the electricity price into dynamic elements and evaluates the impact on the attractiveness of dynamic rates. This is done for both the fiscal levies (dynamic tax) and for the public service obligation (PSO) levy (dynamic PSO).

Accurate short-term load forecasting at disaggregated levels is critical for energy management in microgrids and institutional environments, yet it remains a challenge due to high consumption variability and limited contextual information. This paper proposes a hybrid model that combines Fuzzy ARTMAP neural networks with K-means clustering to improve hourly load forecasting using real data from a university microgrid. The methodology includes key preprocessing steps such as filtering low-load records, removing holidays, interpolating missing values, and applying cyclic encoding to standardize the data into 96 time intervals per day (15-min resolution). For each prediction, the average load profile of the five most recent weekdays is computed and compared to cluster centroids to identify the most similar group, which is then used to train the neural network. Results demonstrate consistent improvements in MAPE, RMSE, and MAE compared to the non-clustered baseline. The model showed robustness to non-stationary behavior and atypical patterns, even when relying solely on timestamp and load data. The proposed strategy outperformed conventional approaches and proved suitable for complex, data-limited environments.

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.