Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

Phuket is a tropical island in Thailand that is famous for tourism. The COVID-19 pandemic resulted in the number of tourists reducing to almost zero. Since tourism contributes around one-half of the gross provincial product of Phuket, the impact was so severe that even the numbers of people employed and registered as locals decreased. Analysing the data from January 2015 to March 2021, we found that the total, residential and non-residential monthly consumptions dropped significantly after Thailand's State of Emergency was declared in March 2020. Unlike other studies that reported residential consumption increasing when people are required to stay home for a prolonged period, Phuket's residential consumption decreased by more than 10% from the pre-COVID-19 level, possibly due to the drop in peer-to-peer accommodation bookings. To study the impact on consumption in detail, we modelled using cascade regression analysis by dividing the predictors into three groups, namely socioeconomics, weather and calendar period. The results showed that the number of guest arrivals was the most statistically significant in all types of consumption and should be used as a predictor for water demand forecasting models in tourism areas.

Caribbean Water Study

The Caribbean Water Study describes the operational and financial performance of selected water utilities in the Caribbean as reported by the utilities as well as secodary sources, the situation of non-revenue water (NRW) among these utilities, the financial impact of COVID-19 on the utilites, and the issue of their resilience to natural disasters. Benchmarking of the key performance indicators for water utilities in the Caribbean shows how utilities are performing in relation to their peers across time. NRW is seen to be one of the biggest challenges for water utilities in the Caribbean and one of the most direct ways to improve a utilitys efficiency, financial performance, and quality of service. In addition, reducing NRW contributes significantly to climate change adaptation. Regarding financial impact of COVID-19, the Study found that due to the large decreases in non-residential consumption, most utilities registered a fall in revenues and in average tariffs. The Study elucidated the fact that their small size and limited resources of water utilities make it is difficult for them to recover from the devastation of a storm on their own and post-disaster response, natural disaster preparedness, investments to increase resiliency, and access to funds are of critical importance.

Natural Capital Accounting Informing Water Management Policies in Europe

In the European Union, the Water Framework Directive provides a roadmap for achieving good water status and sustainable water usage, and a framework for the information, types of analysis, and interventions required by the Member States. Lack of previous knowledge in, and understanding of, interdisciplinary approaches across European countries has led to applications of corrective measures that have yielded less than favourable results. The natural capital paradigm, the assessment and monitoring of the value of natural capital, has the potential to convey information on the use of water resources and improve the connection between implemented measures and changes in the status of the resources, thus enhancing the effectiveness of policy interventions. In this paper, we present the natural capital accounting methodology, adapted to the requirements of the Directive, and demonstrate its application in two European catchments. Using economic methods, the asset value of two ecosystem services was estimated and associated with changes in water status due to policy instruments. Findings demonstrate that the asset value of water for residential consumption and recreational purposes fluctuates from year to year, influenced by current and future uses. Consequently, managing authorities should consider both current and emerging pressures when designing interventions to manage water resource sustainably.

Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration

In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.