Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

A construction contract is a mutually agreed upon and signed document by the contracting parties and plays a significant role in controlling and preventing disputes in a project. This paper’s main objective is to develop a framework to assess sources of ambiguity in construction contracts for comprehensive referencing and early detection of potential root causes of claims and disputes. This is addressed by (1) developing a hierarchical concept map, representing well-rounded sources of contract ambiguities through a comprehensive analysis of the literature; and (2) validating the proposed model by the analysis of relevant court cases at the Supreme Court of Canada (SCC) as the court of last resort in the Canadian judicial system. Starting with 285 relevant cases in construction (from a total of 30,500 cases listed on the SCC website), we defined topical, temporal, and geographic criteria, and narrowed the cases accordingly down to 50, which underwent a first-level analysis, and 13 cases, which went through a detailed analysis. The study found that incomplete and/or redundant information in the contract documents are the core reasons for conflict between contracted parties in the Canadian construction industry. Therefore, proactive identification of sources of ambiguity in contract documents by the general contractor and owner and attentive preparation of contract documents to avoid contradictory information/terms, missing information, or vague clauses can minimize the potential lengthy and costly litigation process at the court level.

Construction renovation projects increase the risk of structural fire, mostly due to the accumulation of combustible construction materials and waste. In particular, when the building remains operational during such projects, the redistribution of occupants and interruptions with access corridors/exit egress can exponentially increase the risk for the occupants. Most construction projects are, however, planned and scheduled merely based on the time and budget criteria. While safety is considered paramount and is meant to be applied as a hard constraint in the scheduling stage, in practice, safe evacuation considerations are reduced to rules of thumb and general code guidelines. In this paper, we propose simulation as a tool to introduce safety under structural fire, as a decision criterion, to be mixed with time and budget for selecting the best construction schedule alternative. We have used the BIM (building information model) to extract the building’s spatial and physical properties; and have applied co-simulation of fire, through computational fluid dynamics (CFD), and occupants’ evacuation behavior, through agent-based modeling (ABM) to estimate the average and maximum required safe egress time for various construction sequencing alternatives. This parameter is then used as a third decision criterion, combined with the project’s cost and duration, to evaluate construction schedule alternatives. We applied our method to a three-floor fire zone in a high-rise educational building in Montreal, and our results show that considering the fire safety criterion can make a difference in the final construction schedule. Our proposed method suggests an additional metric for evaluating renovation projects’ construction plans, particularly in congested buildings which need to remain fully or partially operational during the renovation. Thus, this method can be employed by safety officers and facility managers, as well as construction project planners to guide accounting for fire incidents while planning for these types of projects.